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| Rank,ID,Title,Authors,Year,Venue,Track,Status,Primary Area,Keywords,Citations,BM25 Score,Combined Score,DOI,URL,PDF,Source,TLDR,Abstract | |
| 1,RSvfY6dRVN,Learning and Reusing Abstract Latent Actions in a Hippocampal-Entorhinal-Inspired World Model,,2026,ICLR 2026,main,Active,applications to neuroscience & cognitive science,brain-inspired model;hippocampal-entorhinal coupling;inverse model;latent action;structural generalization;self-supervised learning,0,23.784,0.000,,https://openreview.net/forum?id=RSvfY6dRVN,,offline_iclr,,"Humans are capable of abstracting dynamic experiences into structured representations, facilitating both the inference of shared patterns by observing similar transition dynamics and the transfer of these structures across varied contexts. The hippocampal-entorhinal circuit, widely known for its rol" | |
| 2,eBAMg7w96m,Understanding the Emergence of Seemingly Useless Features in Next-Token Predictors,,2026,ICLR 2026,main,Active,interpretability and explainable AI,next-token prediction;transformers;interpretability,0,22.473,0.000,,https://openreview.net/forum?id=eBAMg7w96m,,offline_iclr,,"Trained Transformers have been shown to compute abstract features that appear redundant for predicting the immediate next token. We identify which components of the gradient signal from the next-token prediction objective give rise to this phenomenon, and we propose a method to estimate the influenc" | |
| 3,czpx02orl7,Learning Abstract World Models for Value-preserving Planning with Options,Rafael Rodriguez-Sanchez; George Konidaris,2024,ICLR 2024,main,Reject,reinforcement learning,Model-based RL;Options;Temporal abstraction;State abstraction;Representation learning;reinforcement learning;MDPs,0,21.979,0.000,,https://openreview.net/forum?id=czpx02orl7,,offline_iclr,,"General-purpose agents require fine-grained controls and rich sensory inputs to perform a wide range of tasks. However, this complexity often leads to intractable decision-making. Traditionally, agents are provided with task-specific action and observation spaces to mitigate this challenge, but this" | |
| 4,a1zfcaNTkM,ExoPredicator: Learning Abstract Models of Dynamic Worlds for Robot Planning,,2026,ICLR 2026,main,Active,"neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)",learning abstractions for planning;neuro-symbolic ai;concept learning,0,21.566,0.000,,https://openreview.net/forum?id=a1zfcaNTkM,,offline_iclr,,"Long‑horizon embodied planning is challenging because the world does not only change through an agent’s actions: exogenous processes (e.g., water heating, dominoes cascading) unfold concurrently with the agent's actions. We propose a framework for abstract world models that jointly learns (i) symbol" | |
| 5,QOfswj7hij,VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planning,Yichao Liang; Nishanth Kumar; Hao Tang; Adrian Weller; Joshua B. Tenenbaum,2025,ICLR 2025,main,Spotlight,"neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)",learning abstractions for planning;neuro-symbolic ai;concept learning,0,21.529,0.000,,https://iclr.cc/virtual/2025/poster/29691,https://openreview.net/pdf?id=QOfswj7hij,offline_iclr,,"Broadly intelligent agents should form task-specific abstractions that selectively expose the essential elements of a task, while abstracting away the complexity of the raw sensorimotor space. In this work, we present Neuro-Symbolic Predicates, a first-order abstraction language that combines the st" | |
| 6,tpbtodnI1p,World Model Implanting for Test-time Adaptation of Embodied Agents,Minjong Yoo; Jinwoo Jang; Sihyung Yoon; Honguk Woo,2025,ICML 2025,main,Poster,reinforcement_learning->everything_else,Embodied AI;Model implanting;World models;Large language model,0,20.976,0.000,,https://icml.cc/virtual/2025/poster/43758,https://openreview.net/pdf?id=tpbtodnI1p,offline_icml,,"In embodied AI, a persistent challenge is enabling agents to robustly adapt to novel domains without requiring extensive data collection or retraining. To address this, we present a world model implanting framework (WorMI) that combines the reasoning capabilities of large language models (LLMs) with" | |
| 7,w8BnKGFIYN,Learning to Play Atari in a World of Tokens,Pranav Agarwal; Sheldon Andrews; Samira Ebrahimi Kahou,2024,ICML 2024,main,Poster,,,0,20.700,0.000,,https://icml.cc/virtual/2024/poster/32760,https://openreview.net/pdf?id=w8BnKGFIYN,offline_icml,,"Model-based reinforcement learning agents utilizing transformers have shown improved sample efficiency due to their ability to model extended context, resulting in more accurate world models. However, for complex reasoning and planning tasks, these methods primarily rely on continuous representation" | |
| 8,GfPwZwZ9xZ,VLASim: World Modelling via VLM-Directed Abstraction and Simulation from a Single Image,,2026,ICLR 2026,main,Active,"applications to computer vision, audio, language, and other modalities",world models;video models;physical simulation;code generation,0,20.434,0.000,,https://openreview.net/forum?id=GfPwZwZ9xZ,,offline_iclr,,"Generative video models, a leading approach to world modeling, face fundamental limitations. They often violate physical and logical rules, lack interactivity, and operate as opaque black boxes ill-suited for building structured, queryable worlds. To overcome these challenges, we propose a new parad" | |
| 9,Bkp_y7qxe,Unsupervised Deep Learning of State Representation Using Robotic Priors,Timothee LESORT; David FILLIAT,2017,ICLR 2017,main,Reject,,Deep learning;Computer vision;Unsupervised Learning,0,19.706,0.000,,https://openreview.net/forum?id=Bkp_y7qxe,,offline_iclr,This paper introduces a method for training a deep neural network to learn a representation of a robot's environment state using a priori knowledge.,"Our understanding of the world depends highly on how we represent it. Using background knowledge about its complex underlying physical rules, our brain can produce intuitive and simplified representations which it can easily use to solve problems. The approach of this paper aims to reproduce this s" | |
| 10,YeTYJz7th5,Learning Temporally AbstractWorld Models without Online Experimentation,Benjamin Freed; Siddarth Venkatraman; Guillaume Adrien Sartoretti; Jeff Schneider; Howie Choset,2023,ICML 2023,main,Poster,,,0,19.504,0.000,,https://icml.cc/virtual/2023/poster/23495,https://openreview.net/pdf?id=YeTYJz7th5,offline_icml,,"Agents that can build temporally abstract representations of their environment are better able to understand their world and make plans on extended time scales, with limited computational power and modeling capacity. However, existing methods for automatically learning temporally abstract world mode" | |
| 11,4627,Improved Distributed Principal Component Analysis,Maria-Florina Balcan; Vandana Kanchanapally; Yingyu Liang; David Woodruff,2014,NIPS 2014,main,Poster,,,0,19.368,0.000,,https://nips.cc/virtual/2014/poster/4627,https://papers.nips.cc/paper_files/paper/2014/file/e968f1646c1c6c35422b64c0934772a4-Paper.pdf,offline_nips,,"We study the distributed computing setting in which there are multiple servers, each holding a set of points, who wish to compute functions on the union of their point sets. A key task in this setting is Principal Component Analysis (PCA), in which the servers would like to compute a low dimensional" | |
| 12,EHmjRIA4l2,Compositional World Models with Interpretable Abstractions,Vishwas Sathish; Rajesh P. N. Rao,2025,ICLR 2025,main,Withdraw,"unsupervised, self-supervised, semi-supervised, and supervised representation learning",State-Action Abstractions;Predictive Coding;Hierarchical Planning;Compositional World Models;Contrastive Learning;Hypernetworks;Hierarchical Reinforcement Learning,0,19.193,0.000,,https://openreview.net/forum?id=EHmjRIA4l2,,offline_iclr,,"We present a modular and compositional approach to learning human-aligned world models via state-action hierarchies. Our approach is inspired by sensory-motor hierarchies in the mammalian brain. We model complex state transition dynamics as a sequence of simpler dynamics, which in turn can be modele" | |
| 13,5f13f76606,Diffeomorphic Dimensionality Reduction,Christian Walder; Bernhard Schölkopf,2008,NIPS 2008,main,Poster,,,0,18.890,0.000,,https://papers.nips.cc/paper_files/paper/2008/hash/647bba344396e7c8170902bcf2e15551-Abstract.html,https://papers.nips.cc/paper_files/paper/2008/file/647bba344396e7c8170902bcf2e15551-Paper.pdf,offline_nips,,This paper introduces a new approach to constructing meaningful lower dimensional representations of sets of data points. We argue that constraining the mapping between the high and low dimensional spaces to be a diffeomorphism is a natural way of ensuring that pairwise distances are approximately p | |
| 14,urueR03mkng,Leveraging the Inductive Bias of Large Language Models for Abstract Textual Reasoning,Christopher Michael Rytting; David Wingate,2021,NIPS 2021,main,Poster,,natural language processing;reasoning;transfer learning;generalization,0,18.429,0.000,,https://nips.cc/virtual/2021/poster/26937,https://openreview.net/pdf?id=urueR03mkng,offline_nips,We characterize the ability of connectionist pre-trained language models to generalize on symbolic reasoning tasks.,"Large natural language models (LMs) (such as GPT-3 or T5) demonstrate impressive abilities across a range of general NLP tasks. Here, we show that the knowledge embedded in such models provides a useful inductive bias, not just on traditional NLP tasks, but also in the nontraditional task of trainin" | |
| 15,6507e53d79,Generation of Internal Representation by α-Transformation,Ryotaro Kamimura,1993,NIPS 1993,main,Poster,,,0,18.181,0.000,,https://papers.nips.cc/paper_files/paper/1993/hash/98d6f58ab0dafbb86b083a001561bb34-Abstract.html,https://papers.nips.cc/paper_files/paper/1993/file/98d6f58ab0dafbb86b083a001561bb34-Paper.pdf,offline_nips,,Abstract Unavailable | |
| 16,lzfzjYuWgY,Do LLMs Build World Representations? Probing Through the Lens of State Abstraction,Zichao Li; Yanshuai Cao; Jackie CK Cheung,2024,NIPS 2024,main,Poster,natural_language_processing,Large Language Models;World Models;World Representation;Probing;Reinforcement Learning;State Abstraction,0,17.930,0.000,,https://neurips.cc/virtual/2024/poster/93786,https://openreview.net/pdf?id=lzfzjYuWgY,offline_nips,,"How do large language models (LLMs) encode the state of the world, including the status of entities and their relations, as described by a text? While existing work directly probes for a complete state of the world, our research explores whether and how LLMs abstract this world state in their intern" | |
| 17,VgtpRXhxli,Efficient Fairness-Performance Pareto Front Computation,Mark Kozdoba; Binyamin Perets; Shie Mannor,2025,ICLR 2025,main,Reject,"alignment, fairness, safety, privacy, and societal considerations",Fairness;fair Representations;Fairness-Performance Pareto Front;Pareto Front;convex concave optimization,0,17.873,0.000,,https://openreview.net/forum?id=VgtpRXhxli,,offline_iclr,,"There is a well known intrinsic trade-off between the fairness of a representation and the performance of classifiers derived from the representation. | |
| Due to the complexity of optimisation algorithms in most modern representation learning approaches, for a given method it may be non-trivial to deci" | |
| 18,KC58bVmxyN,A Cognitive Model for Learning Abstract Relational Structures from Memory-based Decision-Making Tasks,Haruo Hosoya,2024,ICLR 2024,main,Poster,applications to neuroscience & cognitive science,Brain-inspired model; hippocampus; entorhinal cortex; memory; relational representation,0,17.850,0.000,,https://iclr.cc/virtual/2024/poster/18912,https://openreview.net/pdf?id=KC58bVmxyN,offline_iclr,,"Motivated by a recent neuroscientific hypothesis, some theoretical studies have accounted for neural cognitive maps in the rodent hippocampal formation as a representation of the general relational structure across task environments. However, despite their remarkable results, it is unclear whether " | |
| 19,eeeed0d0be,Strategy Grafting in Extensive Games,Kevin Waugh; Nolan Bard; Michael Bowling,2009,NIPS 2009,main,Poster,,,0,17.804,0.000,,https://papers.nips.cc/paper_files/paper/2009/hash/e0ec453e28e061cc58ac43f91dc2f3f0-Abstract.html,https://papers.nips.cc/paper_files/paper/2009/file/e0ec453e28e061cc58ac43f91dc2f3f0-Paper.pdf,offline_nips,,"Extensive games are often used to model the interactions of multiple agents within an environment. Much recent work has focused on increasing the size of an extensive game that can be feasibly solved. Despite these improvements, many interesting games are still too large for such techniques. A co" | |
| 20,gnXTDQyxlU,PIVOT-R: Primitive-Driven Waypoint-Aware World Model for Robotic Manipulation,Kaidong Zhang; Pengzhen Ren; Bingqian Lin; Junfan Lin; Shikui Ma,2024,NIPS 2024,main,Poster,robotics,Robot manipulation; World model,0,17.736,0.000,,https://neurips.cc/virtual/2024/poster/94118,https://openreview.net/pdf?id=gnXTDQyxlU,offline_nips,,"Language-guided robotic manipulation is a challenging task that requires an embodied agent to follow abstract user instructions to accomplish various complex manipulation tasks. Previous work generally maps instructions and visual perceptions directly to low-level executable actions, neglecting the " | |
| 21,3sWghzJvGd,Towards Unraveling and Improving Generalization in World Models,Qiaoyi Fang; Weiyu Du; Hang Wang; Junshan Zhang,2024,NIPS 2024,main,Reject,reinforcement_learning,world models;reinforcement learning;generalization,0,17.686,0.000,,https://openreview.net/forum?id=3sWghzJvGd,,offline_nips,,"World model has recently emerged as a promising approach to reinforcement learning (RL), as evidenced by its great successes that world model based agents exhibit state-of-the-art performance on a wide range visual control tasks. In this study, we aim to first obtain a clear understanding of the gen" | |
| 22,Rm5Qi57C5I,Do Embodied Agents Dream of Pixelated Sheep: Embodied Decision Making using Language Guided World Modelling,Kolby Nottingham; Prithviraj Ammanabrolu; Alane Suhr; Yejin Choi; Hannaneh Hajishirzi,2023,ICML 2023,main,Poster,,,0,17.635,0.000,,https://icml.cc/virtual/2023/poster/24286,https://openreview.net/pdf?id=Rm5Qi57C5I,offline_icml,,"Reinforcement learning (RL) agents typically learn tabula rasa, without prior knowledge of the world. However, if initialized with knowledge of high-level subgoals and transitions between subgoals, RL agents could utilize this Abstract World Model (AWM) for planning and exploration. We propose using" | |
| 23,k7nYm2yU5i,Towards Understanding Robustness and Generalization in World Models,Qiaoyi Fang; Weiyu Du; Hang Wang; Junshan Zhang,2025,ICLR 2025,main,Withdraw,reinforcement learning,World models;robustness;generalization;model-based reinforcement learning,0,17.622,0.000,,https://openreview.net/forum?id=k7nYm2yU5i,,offline_iclr,,"World model has recently emerged as a promising approach to reinforcement learning (RL), as evidenced by the recent successes that world model based agents achieve state-of-the-art performance on a wide range of visual control tasks. This work aims to obtain a deep understanding of the robustness an" | |
| 24,cxKLRM3KhC,Residual Connections Harm Generative Representation Learning,Xiao Zhang; Ruoxi Jiang; Will Gao; Rebecca Willett; Michael Maire,2025,ICLR 2025,main,Reject,"unsupervised, self-supervised, semi-supervised, and supervised representation learning",Decayed Residual Connections;Representation Learning,0,17.600,0.000,,https://openreview.net/forum?id=cxKLRM3KhC,,offline_iclr,,"We show that introducing a weighting factor to reduce the influence of identity shortcuts in residual networks significantly enhances semantic feature learning in generative representation learning frameworks, such as masked autoencoders (MAEs) and diffusion models. Our modification improves linear" | |
| 25,H1gax6VtDB,Contrastive Learning of Structured World Models,Thomas Kipf; Elise van der Pol; Max Welling,2020,ICLR 2020,main,Talk,,state representation learning;graph neural networks;model-based reinforcement learning;relational learning;object discovery,0,17.593,0.000,,https://openreview.net/forum?id=H1gax6VtDB,,offline_iclr,Contrastively-trained Structured World Models (C-SWMs) learn object-oriented state representations and a relational model of an environment from raw pixel input.,"A structured understanding of our world in terms of objects, relations, and hierarchies is an important component of human cognition. Learning such a structured world model from raw sensory data remains a challenge. As a step towards this goal, we introduce Contrastively-trained Structured World Mod" | |
| 26,AvSIqjCWVId,Abstract Visual Reasoning by Self-supervised Contrastive Learning,Weiwen Lu; Aihua Yin; Sidong Wang; Hongzhi You; Ru-Yuan Zhang,2023,ICLR 2023,main,Reject,,,0,17.449,0.000,,https://openreview.net/forum?id=AvSIqjCWVId,,offline_iclr,Demonstration of an unsupervised model to solve analogy reasoning in Raven’s Progressive Matrices task and its variant. ,"Neuro-symbolic models of artificial intelligence (AI) have been recently developed to perform tasks involving abstract visual reasoning that is a hallmark of human intelligence but remains challenging for deep neural network methods. However, most of the current neuro-symbolic models also rely on su" | |
| 27,Pnr8XNWcY0,Roadmap towards Superhuman Speech Understanding using Large Language Models,FanBu; Yuhao Zhang; Xidong Wang; Benyou Wang; Qun Liu,2025,ICLR 2025,main,Withdraw,"applications to computer vision, audio, language, and other modalities",Large langauge models; speech language models;,0,17.391,0.000,,https://openreview.net/forum?id=Pnr8XNWcY0,,offline_iclr,,"The success of large language models (LLMs) has prompted efforts to integrate speech and audio data, aiming to create general foundation models capable of processing both textual and non-textual inputs. Recent advances, such as GPT-4o, highlight the potential for end-to-end speech LLMs, which preser" | |
| 28,8BJl6LQgW5,Visual Representation Learning for World Models by Predicting Fine-Grained Motion,Zhao-Han Peng; Shaohui Li; Zhi Li; Yu LIU; You He,2025,ICLR 2025,main,Withdraw,reinforcement learning,world models;model-based reinforcement learning;visual representation learning,0,17.378,0.000,,https://openreview.net/forum?id=8BJl6LQgW5,,offline_iclr,,"Originating from model-based reinforcement learning (MBRL) methods, algorithms based on world models have been widely applied to boost sample efficiency in visual environments. However, existing world models often struggle with irrelevant background information and omit moving tiny objects that can " | |
| 29,4093,Robust Low Rank Kernel Embeddings of Multivariate Distributions,Le Song; Bo Dai,2013,NIPS 2013,main,Poster,,,0,17.361,0.000,,https://nips.cc/virtual/2013/poster/4093,https://papers.nips.cc/paper_files/paper/2013/file/49b8b4f95f02e055801da3b4f58e28b7-Paper.pdf,offline_nips,,"Kernel embedding of distributions has led to many recent advances in machine learning. However, latent and low rank structures prevalent in real world distributions have rarely been taken into account in this setting. Furthermore, no prior work in kernel embedding literature has addressed the issue " | |
| 30,BgcapX9ers,Hierarchical Object-Oriented POMDP Planning for Object Rearrangement,Rajesh Devaraddi Mangannavar; Alan Fern; Prasad Tadepalli,2025,ICLR 2025,main,Reject,"applications to robotics, autonomy, planning",rearrangement;POMDP;planning;reinforcement learning;object search,0,17.342,0.000,,https://openreview.net/forum?id=BgcapX9ers,,offline_iclr,,"We present an online planning framework for solving multi-object rearrangement problems in partially observable, multi-room environments. Current object rearrangement solutions, primarily based on Reinforcement Learning or hand-coded planning methods, often lack adaptability to diverse challenges. T" | |
| 31,nb3VjILNVs,Low Compute Unlearning via Sparse Representations,Vedant Shah; Frederik Träuble; Ashish Malik; Hugo Larochelle; Michael Curtis Mozer,2025,ICLR 2025,main,Reject,"alignment, fairness, safety, privacy, and societal considerations",Sparse Representations;Discrete Bottlenecks;Model Editing;Unlearning,0,17.322,0.000,,https://openreview.net/forum?id=nb3VjILNVs,,offline_iclr,,"Machine \emph{unlearning}, which involves erasing knowledge about a \emph{forget set} from a trained model, can prove to be | |
| costly and infeasible using existing techniques. We propose a low compute unlearning technique based on a discrete representational bottleneck. We show that the proposed tech" | |
| 32,W7WUJTGByR,Flow Equivariant World Modeling for Partially Observed Dynamic Environments,,2026,ICLR 2026,main,Active,generative models,World Model;Memory;Partial Observability;Equivariance;Structured Representation Learning,0,17.260,0.000,,https://openreview.net/forum?id=W7WUJTGByR,,offline_iclr,,"Embodied systems experience the world as 'a symphony of flows': a combination of many continuous streams of sensory input coupled to self-motion, interwoven with the motion of external objects. These streams obey smooth, time-parameterized symmetries, which combine through a precisely structured alg" | |
| 33,6PcJEFKvBD,offline_rl_ope: A Python package for off-policy evaluation of offline RL models with real world data,Joshua William Spear; Matthieu Komorowski; REBECCA POPE; Neil J Sebire,2025,ICLR 2025,main,Reject,"infrastructure, software libraries, hardware, systems, etc.",Offline RL;OPE;Python;PyTorch,0,17.108,0.000,,https://openreview.net/forum?id=6PcJEFKvBD,,offline_iclr,,offline_rl_ope is a fully unit tested and runtime type checked Python package for performing off-policy evaluation of offline RL models. offline_rl_ope has been designed for OPE workflows using real world data by: naturally handling uneven trajectory lengths; including novel convergence metrics whic | |
| 34,b2u1yrTwFK,Dyn-O: Building Structured World Models with Object-Centric Representations,Zizhao Wang; Kaixin Wang; Li Zhao; Peter Stone; Jiang Bian,2025,NIPS 2025,main,Poster,reinforcement_learning,World Model;Object-centric representation,0,17.100,0.000,,https://openreview.net/forum?id=b2u1yrTwFK,,offline_nips,,"World models aim to capture the dynamics of the environment, enabling agents to predict and plan for future states. In most scenarios of interest, the dynamics are highly centered on interactions among objects within the environment. This motivates the development of world models that operate on obj" | |
| 35,UuchYL8wSZo,Learning Generalizable Visual Representations via Interactive Gameplay,Luca Weihs; Aniruddha Kembhavi; Kiana Ehsani; Sarah M Pratt; Winson Han,2021,ICLR 2021,main,Oral,,representation learning;deep reinforcement learning;computer vision,0,17.047,0.000,,https://iclr.cc/virtual/2021/poster/2685,https://openreview.net/pdf?id=UuchYL8wSZo,offline_iclr,,"A growing body of research suggests that embodied gameplay, prevalent not just in human cultures but across a variety of animal species including turtles and ravens, is critical in developing the neural flexibility for creative problem solving, decision making, and socialization. Comparatively littl" | |
| 36,GfVKK5sKit,LLMs Struggle to Balance Reasoning and World Knowledge in Causal Narrative Understanding,,2026,ICLR 2026,main,Active,"foundation or frontier models, including LLMs",Causal Inference;Large Language Models;Reasoning;Narratives,0,17.030,0.000,,https://openreview.net/forum?id=GfVKK5sKit,,offline_iclr,,"The ability to robustly identify causal relationships is essential for autonomous decision-making and adaptation to novel scenarios. However, accurately inferring causal structure requires integrating both world knowledge and abstract logical reasoning. In this work, we investigate the interaction b" | |
| 37,DeG07_TcZvT,Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task,Kenneth Li; Aspen K Hopkins; David Bau; Fernanda Viégas; Hanspeter Pfister,2023,ICLR 2023,main,Top-5%,,world representation;GPT,0,17.027,0.000,,https://iclr.cc/virtual/2023/poster/11827,https://openreview.net/pdf?id=DeG07_TcZvT,offline_iclr,,"Language models show a surprising range of capabilities, but the source of their apparent competence is unclear. Do these networks just memorize a collection of surface statistics, or do they rely on internal representations of the process that generates the sequences they see? We investigate this q" | |
| 38,LFCSVVIy1x,Can World Models Benefit VLMs for World Dynamics?,,2026,ICLR 2026,main,Active,"applications to computer vision, audio, language, and other modalities",World Model;Multi-modal Large Language Model;Multi-modal Representation Learning,0,16.960,0.000,,https://openreview.net/forum?id=LFCSVVIy1x,,offline_iclr,,"Trained on internet-scale video data, world models are increasingly recognized as powerful world simulators that can generate consistent and plausible dynamics over structure, motion, and physics. While recent studies have explored the few-shot learning capabilities of world models on vision tasks, " | |
| 39,gehXu3kDU1P,Learning Algebraic Representation for Systematic Generalization in Abstract Reasoning,Chi Zhang; Sirui Xie; Baoxiong Jia; Ying Nian Wu; Song-Chun Zhu,2022,ICLR 2022,main,Withdraw,,,0,16.949,0.000,,https://openreview.net/forum?id=gehXu3kDU1P,,offline_iclr,,"Is intelligence realized by connectionist or classicist? While connectionist approaches have achieved superhuman performance, there has been growing evidence that such task-specific superiority is particularly fragile in systematic generalization. This observation lies in the central debate (Fodor &" | |
| 40,OFF38XvszP,Slot Structured World Models,Jonathan Collu; Riccardo Majellaro; Aske Plaat; Thomas M. Moerland,2024,ICLR 2024,main,Withdraw,reinforcement learning,world models;model-based reinforcement learning;object-centric representation learning,0,16.938,0.000,,https://openreview.net/forum?id=OFF38XvszP,,offline_iclr,,The ability to perceive and reason about individual objects enables humans to build a robust understanding of the environment and its dynamics. Replicating such abilities in artificial systems would represent a significant milestone toward building intelligent agents. Contrastive Learning of Structu | |
| 41,jQSBcVURlpW,Learning Algebraic Representation for Abstract Spatial-Temporal Reasoning,Chi Zhang; Sirui Xie; Baoxiong Jia; Yixin Zhu; Ying Nian Wu,2021,ICLR 2021,main,Reject,,,0,16.926,0.000,,https://openreview.net/forum?id=jQSBcVURlpW,,offline_iclr,,"Is intelligence realized by connectionist or classicist? While connectionist approaches have achieved superhuman performance, there has been growing evidence that such task-specific superiority is particularly fragile in systematic generalization. This observation lies in the central debate (Fodor e" | |
| 42,7DtgxVZGj-y,Contrastive Unsupervised Learning of World Model with Invariant Causal Features,Rudra P. K. Poudel; Harit Pandya; Roberto Cipolla,2023,ICLR 2023,main,Reject,,world models;causality;contrastive learning;model-based reinforcement learning;reinforcement learning;out-of-distribution generalisation;sim-to-real transfer;robot navigation,0,16.925,0.000,,https://openreview.net/forum?id=7DtgxVZGj-y,,offline_iclr,"We present a world model, which learns the causal features using invariance principle and achieves state-of-the-art performance on out-of-distribution generalisation.","In this paper we present a world model, which learns the causal features using invariance principle. We use contrastive unsupervised learning to learn the invariant causal features, which enforces invariance across augmentations of irrelevant parts or styles of the observation. Since the world model" | |
| 43,r1e7NgrYvH,DO-AutoEncoder: Learning and Intervening Bivariate Causal Mechanisms in Images,Tianshuo Cong; Dan Peng; Furui Liu; Zhitang Chen,2020,ICLR 2020,main,Reject,,Causality discovery;AutoEncoder;Deep representation learning;Do-calculus,0,16.852,0.000,,https://openreview.net/forum?id=r1e7NgrYvH,,offline_iclr,We propose a new framework for deep representation learning that fully capture bivariate causal relationship in the images.,"Some fundamental limitations of deep learning have been exposed such as lacking generalizability and being vunerable to adversarial attack. Instead, researchers realize that causation is much more stable than association relationship in data. In this paper, we propose a new framework called do-calcu" | |
| 44,6JJq5TW9Mc,Learning World Models with Identifiable Factorization,Yu-Ren Liu; Biwei Huang; Zhengmao Zhu; Honglong Tian; Mingming Gong,2023,NIPS 2023,main,Poster,,Model-based Reinforcement Learning; Causal Representation Learning;,0,16.816,0.000,,https://nips.cc/virtual/2023/poster/72763,https://openreview.net/pdf?id=6JJq5TW9Mc,offline_nips,,"Extracting a stable and compact representation of the environment is crucial for efficient reinforcement learning in high-dimensional, noisy, and non-stationary environments. Different categories of information coexist in such environments -- how to effectively extract and disentangle the informati" | |
| 45,SJxrKgStDH,SCALOR: Generative World Models with Scalable Object Representations,Jindong Jiang*; Sepehr Janghorbani*; Gerard De Melo; Sungjin Ahn,2020,ICLR 2020,main,Poster,,,0,16.794,0.000,,https://openreview.net/forum?id=SJxrKgStDH,,offline_iclr,,"Scalability in terms of object density in a scene is a primary challenge in unsupervised sequential object-oriented representation learning. Most of the previous models have been shown to work only on scenes with a few objects. In this paper, we propose SCALOR, a probabilistic generative world model" | |
| 46,FLPFPYJeVU,Value-Based Deep RL Scales Predictably,Oleh Rybkin; Michal Nauman; Preston Fu; Charlie Victor Snell; Pieter Abbeel,2025,ICML 2025,main,Poster,reinforcement_learning->deep_rl,scaling laws;online reinforcement learning;q-learning,0,16.609,0.000,,https://icml.cc/virtual/2025/poster/45893,https://openreview.net/pdf?id=FLPFPYJeVU,offline_icml,,"Scaling data and compute is critical in modern machine learning. However, scaling also demands _predictability_: we want methods to not only perform well with more compute or data, but also have their performance be predictable from low compute or low data runs, without ever running the large-scale " | |
| 47,18128,FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs,Alekh Agarwal; Sham Kakade; Akshay Krishnamurthy; Wen Sun,2020,NIPS 2020,main,Oral,,,0,16.602,0.000,,https://nips.cc/virtual/2020/poster/18128,https://papers.nips.cc/paper_files/paper/2020/file/e894d787e2fd6c133af47140aa156f00-Paper.pdf,offline_nips,,"In order to deal with the curse of dimensionality in reinforcement learning (RL), it is common practice to make parametric assumptions where values or policies are functions of some low dimensional feature space. This work focuses on the representation learning question: how can we learn such featur" | |
| 48,,Learning Abstract World Models with a Group-Structured Latent Space,Thomas Delliaux; Nguyen-Khanh Vu; Vincent François-Lavet; Elise van der Pol; Emmanuel Rachelson,2025,arXiv.org,,,,,0,0.000,0.000,10.48550/arXiv.2506.01529,https://www.semanticscholar.org/paper/ed7346dcb1fe387a64d5789c79cada4bd42205d4,,semantic_scholar,,"Learning meaningful abstract models of Markov Decision Processes (MDPs) is crucial for improving generalization from limited data. In this work, we show how geometric priors can be imposed on the low-dimensional representation manifold of a learned transition model. We incorporate known symmetric st" | |
| 49,,Goal-VLA: Image-Generative VLMs as Object-Centric World Models Empowering Zero-shot Robot Manipulation,Haonan Chen; Jingxiang Guo; Bangjun Wang; Tianrui Zhang; Xuchuan Huang,2025,,,,,,1,0.000,0.000,,https://www.semanticscholar.org/paper/ffccb1b3247f4e63d1b0c8b8c31c70cb0db16e3d,,semantic_scholar,,"Generalization remains a fundamental challenge in robotic manipulation. To tackle this challenge, recent Vision-Language-Action (VLA) models build policies on top of Vision-Language Models (VLMs), seeking to transfer their open-world semantic knowledge. However, their zero-shot capability lags signi" | |
| 50,,Simple Orthogonal Graph Representation Learning (Student Abstract),Taoyong Cui; Yuhan Dong,2024,AAAI Conference on Artificial Intelligence,,,,,1,0.000,0.000,10.1609/aaai.v38i21.30430,https://www.semanticscholar.org/paper/db73d1cb2c06a45537250c717efe90d5bb7d828a,https://ojs.aaai.org/index.php/AAAI/article/download/30430/32509,semantic_scholar,,"Graph neural networks (GNNs) have attracted significant interest recently since they can effectively process and analyze graph-structured data commonly found in real-world applications. However, the predicament that GNNs are difficult to train becomes worse as the layers increase. The essence of thi" | |
| 51,,World Models and Attention for Reinforcement Learning,David R Ha,2021,IEEE Symposium on Artificial Life,,,,,0,0.000,0.000,10.1162/isal_a_00471,https://www.semanticscholar.org/paper/61df4b1589e0c96df667ab634c4764170903a064,,semantic_scholar,,"Keynote Abstract Consciousness and the concept of internal mental models are foundational topics in neuroscience and psychology. Yet, we do not understand them well enough to engineer artificial lifeforms that are conscious. In this talk, I will be discussing a line of work on developing “world model" | |
| 52,,Sparse representation learning with modified q-VAE towards minimal realization of world model,Taisuke Kobayashi; Ryoma Watanuki,2022,Adv. Robotics,,,,,7,0.000,0.000,10.1080/01691864.2023.2221715,https://www.semanticscholar.org/paper/cd5baf2998cbd3f8ff584179f9b290ce9a1be6ff,https://arxiv.org/pdf/2208.03936,semantic_scholar,,"Extraction of low-dimensional latent space from high-dimensional observation data is essential to construct a real-time robot controller with a world model on the extracted latent space. However, there is no established method for tuning the dimension size of the latent space automatically, sufferin" | |
| 53,,AtResNet: Residual Atrous CNN with Multi-scale Feature Representation for Low Complexity Acoustic Scene Classification,Aswathy Madhu; Suresh Kumaraswamy,2022,"Circuits, systems, and signal processing",,,,,2,0.000,0.000,10.1007/s00034-022-02107-2,https://www.semanticscholar.org/paper/5c0135a85e0cd03693391008e7a1bcfc1538b982,,semantic_scholar,, | |
| 54,,"Abstract 2145: Genetic ancestry associations with pancreatic cancer mutational profiles from a diverse 9,274-patient real-world cohort",Brooke Rhead; Vignesh Vudatha; Y. Pouliot; Edward Williamns; A. Riner,2024,Cancer Research,,,,,0,0.000,0.000,10.1158/1538-7445.am2024-2145,https://www.semanticscholar.org/paper/d21d17242525dc6e87699b8f24edea5c052703d3,,semantic_scholar,," | |
| Pancreatic cancer is an aggressive malignancy associated with racial/ethnic disparities. Notably, Black patients have a higher incidence and mortality compared to their White counterparts. However, low representation of Black and Hispanic patients in cancer genomics studies hampers our understandi" | |
| 55,,HYBRID DEEP LEARNING MODELS FOR MULTILINGUAL SENTIMENT ANALYSIS IN LOW-RESOURCE LANGUAGES,Hendri Purnomo; Randi Estian Pambudi; Danang Ade Muktiawan; Sylvia Sylvia,2025,Coding: Journal of Computing and Software Engineering,,,,,0,0.000,0.000,10.25181/coding.v1i1.4306,https://www.semanticscholar.org/paper/8fe02571bdbc8699903f54cc2ccfdfc52ed2e488,,semantic_scholar,,"Multilingual sentiment analysis poses significant challenges, especially in the context of languages with low resources. The study proposes a hybrid deep learning model based on the CNN-BiLSTM architecture to classify sentiment in multiple languages, including those with limited corpus and lexical r" | |
| 56,,"Abstract C043: | |
| PIN1 | |
| mRNA expression in biliary tract cancer: A multiomic analysis of its prognostic relevance and association with tumor-immune states in a large real-world cohort",Justin H. Lo; Thatcher R Heumann; Denise Shieh; Stamatina Fragkogianni; Brooke Rhead,2025,Molecular Cancer Therapeutics,,,,,0,0.000,0.000,10.1158/1535-7163.targ-25-c043,https://www.semanticscholar.org/paper/aa777000708a4a6f6981d9aa45160715148365a2,,semantic_scholar,," | |
| PIN1, a peptidyl-prolyl isomerase, is overexpressed in many tumor types. Previous studies in pancreatic cancer have shown an association between higher PIN1 expression with an immunosuppressive tumor microenvironment (TME) and poor clinical outcomes. Inhibition of PIN1 in animal models renders" | |
| 57,,Exploring the footprint representation of microwave radiance observations in an Arctic limited-area data assimilation system,M. Mile; S. Guedj; R. Randriamampianina,2024,Geoscientific Model Development,,,,,0,0.000,0.000,10.5194/gmd-17-6571-2024,https://www.semanticscholar.org/paper/7f1e538713569c8334654bd265f3021f606a2311,,semantic_scholar,,"Abstract. The microwave radiances are key observations, especially over data-sparse regions, for operational data assimilation in numerical weather prediction (NWP). An often applied simplification is that these observations are used as point measurements; however, the satellite field of view may co" | |
| 58,,Comprehensive Study on Performance Evaluation and Optimization of Model Compression: Bridging Traditional Deep Learning and Large Language Models,Aayush Saxena; Arit Kumar Bishwas; Ayush Ashok Mishra; Ryan Armstrong,2024,arXiv.org,,,,,5,0.000,0.000,10.48550/arXiv.2407.15904,https://www.semanticscholar.org/paper/b5e760c391804e66b610b5753e193eb5c5acf2ba,,semantic_scholar,,"Deep learning models have achieved tremendous success in most of the industries in recent years. The evolution of these models has also led to an increase in the model size and energy requirement, making it difficult to deploy in production on low compute devices. An increase in the number of connec" | |
| 59,,A Fast Anomaly Detection in Hyperspectral Images Based on Low-Rank and Sparse Representation,Yunchang Wang; Yifan Fu; Zebin Wu; Chengxun He; Peng Zheng,2025,IEEE International Geoscience and Remote Sensing Symposium,,,,,0,0.000,0.000,10.1109/IGARSS55030.2025.11243679,https://www.semanticscholar.org/paper/f4306a0caec2823a3f52a6cef1e90c92615d4ef2,,semantic_scholar,,"The low-rank and sparse representation (LRASR) model has emerged as a powerful tool for hyperspectral image anomaly detection in real-world applications. However, the prohibitive computational complexity of large-scale singular value decomposition (SVD) limits the scalability of LRASR, making it inc" | |
| 60,,Granular-ball representation-based two-stage deep learning model for text classification,Wenbin Qian; Ying He; Xingxing Cai; Jintao Huang,2025,Applied intelligence (Boston),,,,,0,0.000,0.000,10.1007/s10489-025-07010-2,https://www.semanticscholar.org/paper/5dd43a62ace6c47d39fdc4c84cd75ac7df4954d7,,semantic_scholar,, | |
| 61,,Selecting and weighting dynamical models using data-driven approaches,P. Le Bras; F. Sévellec; P. Tandeo; Juan Ruiz; P. Ailliot,2024,Nonlinear Processes in Geophysics,,,,,2,0.000,0.000,10.5194/npg-31-303-2024,https://www.semanticscholar.org/paper/5463e75fe76900d223892f983a8d12b46ef296f1,,semantic_scholar,,"Abstract. In geosciences, multi-model ensembles are helpful to explore the robustness of a range of results. To obtain a synthetic and improved representation of the studied dynamic system, the models are usually weighted. The simplest method, namely the model democracy, gives equal weights to all m" | |
| 62,,Robust auto-weighted and dual-structural representation learning for image clustering,Kun Jiang; Zhaoli Liu; Qindong Sun,2024,J. Electronic Imaging,,,,,1,0.000,0.000,10.1117/1.JEI.33.4.043039,https://www.semanticscholar.org/paper/7700b45f828ce3f9c106f38f79ef5c626ef090a1,,semantic_scholar,,"Abstract. High-dimensional data samples tend to contain highly correlated features and are quite fragile to various noises and outliers in practical applications. For subspace clustering models, it has appeared to be inadequate to adopt conventional norm-based distance measurements to resist feature" | |
| 63,,Survey on Embedding Models for Knowledge Graph and its Applications,Manita Pote,2024,arXiv.org,,,,,2,0.000,0.000,10.48550/arXiv.2404.09167,https://www.semanticscholar.org/paper/cda81da1f54f9d9854e28e2e9b289b7199ea4e59,,semantic_scholar,,"Knowledge Graph (KG) is a graph based data structure to represent facts of the world where nodes represent real world entities or abstract concept and edges represent relation between the entities. Graph as representation for knowledge has several drawbacks like data sparsity, computational complexi" | |
| 64,,IoT Device Authentication via RAM Trace Analysis: A Representation Learning Framework,Asif Iqbal; M. Aman; Biplab Sikdar,2024,Global Communications Conference,,,,,0,0.000,0.000,10.1109/GLOBECOM52923.2024.10901561,https://www.semanticscholar.org/paper/920cd3e808876d817a3caab7800c8c1c4d39279f,,semantic_scholar,,"Recent advances in IoT, machine learning, and edge computing have driven transformative paradigms like smart cities, grids, healthcare, and transportation systems, providing efficient solutions. This has led to a pervasive proliferation of connected devices, ranging from high-power computers to low-" | |
| 65,,ReGA: Representation-Guided Abstraction for Model-based Safeguarding of LLMs,Zeming Wei; Chengcan Wu; Meng Sun,2025,arXiv.org,,,,,3,0.000,0.000,10.48550/arXiv.2506.01770,https://www.semanticscholar.org/paper/dcc59902da1b95f1f63d5f2836327822f94a276a,,semantic_scholar,,"Large Language Models (LLMs) have achieved significant success in various tasks, yet concerns about their safety and security have emerged. In particular, they pose risks in generating harmful content and vulnerability to jailbreaking attacks. To analyze and monitor machine learning models, model-ba" | |
| 66,,Low-Rank Sparse Generative Adversarial Unsupervised Domain Adaptation for Multitarget Traffic Scene Semantic Segmentation,M. Saffari; Mahdi Khodayar,2024,IEEE Transactions on Industrial Informatics,,,,,9,0.000,0.000,10.1109/TII.2023.3291402,https://www.semanticscholar.org/paper/3520845cd4900a7f4f9b82771166bd82f9282394,,semantic_scholar,,"Semantic segmentation in diverse real-world traffic scenes is a challenging task for autonomous vehicles to have a reliable understanding of the outside environment. Although deep visual models show a remarkable performance on traffic scene segmentation, they need significant improvements when the s" | |
| 67,,SETUP: Sentence-level English-To-Uniform Meaning Representation Parser,Emma Markle; Javier Gutierrez Bach; Shira Wein,2025,,,,,,0,0.000,0.000,,https://www.semanticscholar.org/paper/ff0efe591c06f8be2e5cec944f348dee161ad71e,,semantic_scholar,,"Uniform Meaning Representation (UMR) is a novel graph-based semantic representation which captures the core meaning of a text, with flexibility incorporated into the annotation schema such that the breadth of the world's languages can be annotated (including low-resource languages). While UMR shows " | |
| 68,,Recommending Learning Objects through Attentive Heterogeneous Graph Convolution and Operation- Aware Neural Network (Extended Abstract),Yifan Zhu; Qika Lin; Hao Lu; Kaize Shi; Donglei Liu,2024,IEEE International Conference on Data Engineering,,,,,1,0.000,0.000,10.1109/ICDE60146.2024.00505,https://www.semanticscholar.org/paper/15646e51a590f658b99f4267080c2142d5a7a18f,,semantic_scholar,,"Currently, the increasing information overload on Massive Open Online Courses(MOOCs) inhibits the appropriate choice of learning objects by learners, leading to low efficiency and high dropout rates. However, in MOOC platforms, recommendation network structures that can selectively extract implicit " | |
| 69,,Evaluating and Optimizing the Effectiveness of Neural Machine Translation in Supporting Code Retrieval Models: A Study on the CAT Benchmark,H. Phan; Ali Jannesari,2023,International Conference on Information and Knowledge Management,,,,,3,0.000,0.000,10.1145/3583780.3614869,https://www.semanticscholar.org/paper/f6d8199f41015fe6cbba03dc513cfae5a9f663e8,https://dl.acm.org/doi/pdf/10.1145/3583780.3614869,semantic_scholar,,Neural Machine Translation (NMT) is widely applied in software engineering tasks. The effectiveness of NMT for code retrieval relies on the ability to learn from the sequence of tokens in the source language to the sequence of tokens in the target language. While NMT performs well in pseudocode-to-c | |
| 70,,Iris matching recognition based on information quantity and complex domain in low-computational power scenarios,Shuai Liu,2025,J. Electronic Imaging,,,,,0,0.000,0.000,10.1117/1.JEI.34.5.053044,https://www.semanticscholar.org/paper/7bbf7f2f1f1f65a3ef0153c0e7c3c82e9cd6e6e6,,semantic_scholar,,"Abstract. Iris recognition faces challenges from uncertainties in image acquisition, which limit the effective expression of information. Existing recognition models built on deep learning structures have rigid requirements for high-performance computing resources, restricting their application scop" | |
| 71,,Abstract TP171: Automated Large Vessel Occlusions Detection: Improved Diagnostic Accuracy in MCA M2 Segment Using Deep Learning,A. Franciosini; C. Avare; Peter D. Chang; D. Chow; Christopher G Filippi,2025,Stroke,,,,,1,0.000,0.000,10.1161/str.56.suppl_1.tp171,https://www.semanticscholar.org/paper/ffbc835b101ea4c84decae0f4da9f8edab486a5e,,semantic_scholar,," | |
| Introduction: | |
| Integrating AI into clinical practice enables precise diagnosis and could potentially enhance outcomes for patients with large vessel occlusion (LVO). Although advancements in AI have shown promise in enhancing diagnostic accuracy, several studies have reported very low sensitivitie" | |
| 72,,A Switched-Capacitor Compute-in-Memory Ising Hardware Annealing Accelerator,Ariane Hannon; Viet Nguyen; R. B. Staszewski,2025,Irish Signals and Systems Conference,,,,,0,0.000,0.000,10.1109/ISSC67739.2025.11291378,https://www.semanticscholar.org/paper/e9c190a7eedc4c090ebe8cee84c16586b3a97f28,,semantic_scholar,,Ising hardware accelerators are needed to efficiently solve the pervasive real-world combinatorial optimisation problems (COPs) that would otherwise be intractable to solve using brute-force approaches on a traditional computer. The Ising model abstracts a COP to the interactions between a network o | |
| 73,,SpaceTimePilot: Generative Rendering of Dynamic Scenes Across Space and Time,Zhening Huang; Hyeonho Jeong; Xuelin Chen; Yulia Gryaditskaya; Tuanfeng Y. Wang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25075v1,https://arxiv.org/pdf/2512.25075v1,arxiv,,"We present SpaceTimePilot, a video diffusion model that disentangles space and time for controllable generative rendering. Given a monocular video, SpaceTimePilot can independently alter the camera viewpoint and the motion sequence within the generative process, re-rendering the scene for continuous" | |
| 74,,Randomization Times under Quantum Chaotic Hamiltonian Evolution,Souradeep Ghosh; Nicholas Hunter-Jones; Joaquin F. Rodriguez-Nieva,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25074v1,https://arxiv.org/pdf/2512.25074v1,arxiv,,"Randomness generation through quantum-chaotic evolution underpins foundational questions in statistical mechanics and applications across quantum information science, including benchmarking, tomography, metrology, and demonstrations of quantum computational advantage. While statistical mechanics suc" | |
| 75,,Edit3r: Instant 3D Scene Editing from Sparse Unposed Images,Jiageng Liu; Weijie Lyu; Xueting Li; Yejie Guo; Ming-Hsuan Yang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25071v1,https://arxiv.org/pdf/2512.25071v1,arxiv,,"We present Edit3r, a feed-forward framework that reconstructs and edits 3D scenes in a single pass from unposed, view-inconsistent, instruction-edited images. Unlike prior methods requiring per-scene optimization, Edit3r directly predicts instruction-aligned 3D edits, enabling fast and photorealisti" | |
| 76,,Coordinated Humanoid Manipulation with Choice Policies,Haozhi Qi; Yen-Jen Wang; Toru Lin; Brent Yi; Yi Ma,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25072v1,https://arxiv.org/pdf/2512.25072v1,arxiv,,"Humanoid robots hold great promise for operating in human-centric environments, yet achieving robust whole-body coordination across the head, hands, and legs remains a major challenge. We present a system that combines a modular teleoperation interface with a scalable learning framework to address t" | |
| 77,,Scaling Open-Ended Reasoning to Predict the Future,Nikhil Chandak; Shashwat Goel; Ameya Prabhu; Moritz Hardt; Jonas Geiping,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25070v1,https://arxiv.org/pdf/2512.25070v1,arxiv,,"High-stakes decision making involves reasoning under uncertainty about the future. In this work, we train language models to make predictions on open-ended forecasting questions. To scale up training data, we synthesize novel forecasting questions from global events reported in daily news, using a f" | |
| 78,,Classification of Interacting Topological Crystalline Superconductors in Three Dimensions and Beyond,Shang-Qiang Ning; Xing-Yu Ren; Qing-Rui Wang; Yang Qi; Zheng-Cheng Gu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25069v1,https://arxiv.org/pdf/2512.25069v1,arxiv,,"Although classification for free-fermion topological superconductors (TSC) is established, systematically understanding the classification of 3D interacting TSCs remains difficult, especially those protected by crystalline symmetries like the 230 space groups. We build up a general framework for sys" | |
| 79,,No-cost Bell Nonlocality Certification from Quantum Tomography and Its Applications in Quantum Magic Witnessing,Pawel Cieslinski; Lukas Knips; Harald Weinfurter; Wieslaw Laskowski,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25068v1,https://arxiv.org/pdf/2512.25068v1,arxiv,,"Tomographic measurements are the standard tool for characterizing quantum states, yet they are usually regarded only as means for state reconstruction or fidelity measurement. Here, we show that the same Pauli-basis measurements (X, Y, Z) can be directly employed for the certification of nonlocality" | |
| 80,,FineTec: Fine-Grained Action Recognition Under Temporal Corruption via Skeleton Decomposition and Sequence Completion,Dian Shao; Mingfei Shi; Like Liu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25067v1,https://arxiv.org/pdf/2512.25067v1,arxiv,,"Recognizing fine-grained actions from temporally corrupted skeleton sequences remains a significant challenge, particularly in real-world scenarios where online pose estimation often yields substantial missing data. Existing methods often struggle to accurately recover temporal dynamics and fine-gra" | |
| 81,,From Inpainting to Editing: A Self-Bootstrapping Framework for Context-Rich Visual Dubbing,Xu He; Haoxian Zhang; Hejia Chen; Changyuan Zheng; Liyang Chen,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25066v1,https://arxiv.org/pdf/2512.25066v1,arxiv,,"Audio-driven visual dubbing aims to synchronize a video's lip movements with new speech, but is fundamentally challenged by the lack of ideal training data: paired videos where only a subject's lip movements differ while all other visual conditions are identical. Existing methods circumvent this wit" | |
| 82,,Vulcan: Instance-Optimal Systems Heuristics Through LLM-Driven Search,Rohit Dwivedula; Divyanshu Saxena; Sujay Yadalam; Daehyeok Kim; Aditya Akella,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25065v1,https://arxiv.org/pdf/2512.25065v1,arxiv,,"Resource-management tasks in modern operating and distributed systems continue to rely primarily on hand-designed heuristics for tasks such as scheduling, caching, or active queue management. Designing performant heuristics is an expensive, time-consuming process that we are forced to continuously g" | |
| 83,,Feeling Blue: Constructing a Robust SALT3 UV Template and Constraining its Redshift Dependency,Qinan Wang; David O. Jones; Justin D. R. Pierel; Matthew R. Siebert; W. D'Arcy Kenworthy,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25064v1,https://arxiv.org/pdf/2512.25064v1,arxiv,,"Upcoming cosmological surveys will obtain numerous rest-frame ultraviolet (UV) observations of Type Ia supernovae (SNe Ia), yet there is concern about how standardizable SNe Ia are in the UV. In this work, we train a robust optical--UV SED model for SNe Ia (SALT3-UV) with the open-source model-train" | |
| 84,,Many Minds from One Model: Bayesian Transformers for Population Intelligence,Diji Yang; Yi Zhang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25063v1,https://arxiv.org/pdf/2512.25063v1,arxiv,,"Despite their scale and success, modern transformers are almost universally trained as single-minded systems: optimization produces one deterministic set of parameters, representing a single functional hypothesis about the data. Motivated by the idea that intelligence emerge from many minds, we prop" | |
| 85,,On the geometry and topology of representations: the manifolds of modular addition,Gabriela Moisescu-Pareja; Gavin McCracken; Harley Wiltzer; Vincent Létourneau; Colin Daniels,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25060v1,https://arxiv.org/pdf/2512.25060v1,arxiv,,"The Clock and Pizza interpretations, associated with architectures differing in either uniform or learnable attention, were introduced to argue that different architectural designs can yield distinct circuits for modular addition. In this work, we show that this is not the case, and that both unifor" | |
| 86,,Reliable and Resilient Collective Communication Library for LLM Training and Serving,Wei Wang; Nengneng Yu; Sixian Xiong; Zaoxing Liu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25059v1,https://arxiv.org/pdf/2512.25059v1,arxiv,,"Modern ML training and inference now span tens to tens of thousands of GPUs, where network faults can waste 10--15\% of GPU hours due to slow recovery. Common network errors and link fluctuations trigger timeouts that often terminate entire jobs, forcing expensive checkpoint rollback during training" | |
| 87,,Sequential Bayesian parameter-state estimation in dynamical systems with noisy and incomplete observations via a variational framework,Liliang Wang; Alex Gorodetsky,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25056v1,https://arxiv.org/pdf/2512.25056v1,arxiv,,"Online joint estimation of unknown parameters and states in a dynamical system with uncertainty quantification is crucial in many applications. For example, digital twins dynamically update their knowledge of model parameters and states to support prediction and decision-making. Reliability and comp" | |
| 88,,Context-aware LLM-based AI Agents for Human-centered Energy Management Systems in Smart Buildings,Tianzhi He; Farrokh Jazizadeh,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25055v1,https://arxiv.org/pdf/2512.25055v1,arxiv,,This study presents a conceptual framework and a prototype assessment for Large Language Model (LLM)-based Building Energy Management System (BEMS) AI agents to facilitate context-aware energy management in smart buildings through natural language interaction. The proposed framework comprises three | |
| 89,,Emergence of 3D Superconformal Ising Criticality on the Fuzzy Sphere,Yin Tang; Cristian Voinea; Liangdong Hu; Zlatko Papić; W. Zhu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25054v1,https://arxiv.org/pdf/2512.25054v1,arxiv,,"Supersymmetric conformal field theories (SCFTs) form a unique subset of quantum field theories which provide powerful insights into strongly coupled critical phenomena. Here, we present a microscopic and non-perturbative realization of the three-dimensional $\mathcal{N}=1$ superconformal Ising criti" | |
| 90,,Fluid dynamics as intersection problem,Nikita Nekrasov; Paul Wiegmann,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25053v1,https://arxiv.org/pdf/2512.25053v1,arxiv,,"We formulate the covariant hydrodynamics equations describing the fluid dynamics as the problem of intersection theory on the infinite dimensional symplectic manifold associated with spacetime. This point of view separates the structures related to the equation of state, the geometry of spacetime, a" | |
| 91,,The PDE-ODI principle and cylindrical mean curvature flows,Richard H. Bamler; Yi Lai,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25050v1,https://arxiv.org/pdf/2512.25050v1,arxiv,,"We introduce a new approach for analyzing ancient solutions and singularities of mean curvature flow that are locally modeled on a cylinder. Its key ingredient is a general mechanism, called the \emph{PDE--ODI principle}, which converts a broad class of parabolic differential equations into systems " | |
| 92,,Arithmetic with spatiotemporal optical vortex of integer and fractional topological charges,Hsiao-Chih Huang; Chen-Ting Liao; Hui Min Leung,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25049v1,https://arxiv.org/pdf/2512.25049v1,arxiv,,"Spatiotemporal optical vortices carry transverse orbital angular momentum (t-OAM), which give rise to spatiotemporal topological charge (ST-TC). To unleash the full potential of t-OAM in expanding the capacity of communication and computing, we demonstrate the first optical information-processing pi" | |
| 93,,All optical Lithography for Spatiotemporal Patterning of Azopolymer Microreliefs,I Komang Januariyasa; Francesco Reda; Nikolai Liubimtsev; Marina Saphiannikova; Fabio Borbone,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25048v1,https://arxiv.org/pdf/2512.25048v1,arxiv,,"Microstructured surfaces are central to photonics, biointerfaces, and functional coatings, yet they are typically fabricated through multi-step lithographic workflows requiring masks or molds and post-processing. Azopolymers provide an alternative route by converting structured optical fields into s" | |
| 94,,Amplitude constraints on dark energy,Scott Melville,2025,arXiv,,,,,0,0.000,0.000,10.58027/3q8k-ew90,http://arxiv.org/abs/2512.25047v1,https://arxiv.org/pdf/2512.25047v1,arxiv,,"This talk gives a short introduction to the ``UV/EFT correspondence"", which uses scattering amplitudes to relate the Effective Field Theory (EFT) coefficients probed by low-energy measurements to properties of the underlying high-energy (UV) completion. This includes recent ``positivity bounds"" on E" | |
| 95,,Extreme nonlinear optics in optical fibers,Mario Ferraro; Bertrand Kibler; Pierre Béjot; Frédéric Gérome; Benoit Debord,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25046v1,https://arxiv.org/pdf/2512.25046v1,arxiv,,"This paper reviews the field of extreme nonlinear optics in optical fibers, highlighting key phenomena and advancements. It discusses multiple ionization effects caused by femtosecond laser pulses that generate plasma and induce permanent material modifications, as well as plasma luminescence and it" | |
| 96,,Bayesian Elastic Net Regression with Structured Prior Dependence,Christopher M. Hans; Ningyi Liu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25045v1,https://arxiv.org/pdf/2512.25045v1,arxiv,,"Many regularization priors for Bayesian regression assume the regression coefficients are a priori independent. In particular this is the case for standard Bayesian treatments of the lasso and the elastic net. While independence may be reasonable in some data-analytic settings, incorporating depende" | |
| 97,,Compound Estimation for Binomials,Yan Chen; Lihua Lei,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25042v1,https://arxiv.org/pdf/2512.25042v1,arxiv,,"Many applications involve estimating the mean of multiple binomial outcomes as a common problem -- assessing intergenerational mobility of census tracts, estimating prevalence of infectious diseases across countries, and measuring click-through rates for different demographic groups. The most standa" | |
| 98,,Humans rationally balance detailed and temporally abstract world models,Ari E. Kahn; N. D. Daw,2024,bioRxiv,,,,,3,0.000,0.000,10.1038/s44271-024-00169-3,https://www.semanticscholar.org/paper/fec10312b2801c11469ff0da2aad3a9a5bf7a82d,,semantic_scholar,,"How do people model the world’s dynamics to guide mental simulation and evaluate choices? One prominent approach, the Successor Representation (SR), takes advantage of temporal abstraction of future states: by aggregating trajectory predictions over multiple timesteps, the brain can avoid the costs " | |
| 99,,Neurosymbolic Graph Enrichment for Grounded World Models,S. D. Giorgis; Aldo Gangemi; Alessandro Russo,2024,Information Processing & Management,,,,,7,0.000,0.000,10.48550/arXiv.2411.12671,https://www.semanticscholar.org/paper/df0f3aff33dbb48b8d095bd09b746108f7b243e4,,semantic_scholar,,The development of artificial intelligence systems capable of understanding and reasoning about complex real-world scenarios is a significant challenge. In this work we present a novel approach to enhance and exploit LLM reactive capability to address complex problems and interpret deeply contextual | |
| 100,,KARNet: Kalman Filter Augmented Recurrent Neural Network for Learning World Models in Autonomous Driving Tasks,Hemanth Manjunatha; A. Pak; Dimitar Filev; P. Tsiotras,2023,arXiv.org,,,,,5,0.000,0.000,10.48550/arXiv.2305.14644,https://www.semanticscholar.org/paper/f5452f1540fa0cd15a84f90f6b6e356d9e4735ed,http://arxiv.org/pdf/2305.14644,semantic_scholar,,Autonomous driving has received a great deal of attention in the automotive industry and is often seen as the future of transportation. The development of autonomous driving technology has been greatly accelerated by the growth of end-to-end machine learning techniques that have been successfully us | |
| 101,,Abstract 3519: Reconstructing a latent representation of gene expression from genomic alterations to improve clinical utility of real-world clinicogenomics data,Maayan Baron; Sunil Kumar; Felicia Kuperwaser; Dillon Tracy; Emily Vucic,2024,Cancer Research,,,,,0,0.000,0.000,10.1158/1538-7445.am2024-3519,https://www.semanticscholar.org/paper/ffec34481db10375060930315082ce069d9ae97f,,semantic_scholar,," | |
| Background: Molecularly and clinically well-annotated patient datasets are ideal for studying tumor biology and developing robust machine learning (ML) models for predicting outcome and treatment response. These data however rarely exist in real-world settings or in sufficient quantities within re" | |
| 102,,Scaling Cross-Embodiment World Models for Dexterous Manipulation,Zihao He; Bo Ai; Tongzhou Mu; Yulin Liu; Weikang Wan,2025,arXiv.org,,,,,1,0.000,0.000,10.48550/arXiv.2511.01177,https://www.semanticscholar.org/paper/9cafcf8cb712f3b1e250f750d1031e83a0cd70bb,,semantic_scholar,,"Cross-embodiment learning seeks to build generalist robots that operate across diverse morphologies, but differences in action spaces and kinematics hinder data sharing and policy transfer. This raises a central question: Is there any invariance that allows actions to transfer across embodiments? We" | |
| 103,,SketchAA: Abstract Representation for Abstract Sketches,Lan Yang; Kaiyue Pang; Honggang Zhang; Yi-Zhe Song,2021,IEEE International Conference on Computer Vision,,,,,24,0.000,0.000,10.1109/ICCV48922.2021.00994,https://www.semanticscholar.org/paper/53f14a3c0cdc26793b32a17cad8687b8530660cd,,semantic_scholar,,"What makes free-hand sketches appealing for humans lies with its capability as a universal tool to depict the visual world. Such flexibility at human ease, however, introduces abstract renderings that pose unique challenges to computer vision models. In this paper, we propose a purpose-made sketch r" | |
| 104,,Synthesis of historical reservoir operations from 1980 to 2020 for the evaluation of reservoir representation in large-scale hydrologic models,J. Steyaert; L. Condon,2024,Hydrology and Earth System Sciences,,,,,10,0.000,0.000,10.5194/hess-28-1071-2024,https://www.semanticscholar.org/paper/27ac5dcff3b47e1513433d72014da40d7d1f8438,,semantic_scholar,,"Abstract. All the major river systems in the contiguous United States (CONUS) (and many in the world) are impacted by dams, yet reservoir operations remain difficult to quantify and model due to a lack of data. Reservoir operation data are often inaccessible or distributed across many local operatin" | |
| 105,,Leveraging Open‐Source Geographic Databases to Enhance the Representation of Landscape Heterogeneity in Ecological Models,T. A. Gelmi-Candusso; Peter S. Rodriguez; M. Fidino; Kim Rivera; E. Lehrer,2024,Ecology and Evolution,,,,,5,0.000,0.000,10.1002/ece3.70402,https://www.semanticscholar.org/paper/336a02471c8be13486b67937982d2d49f3d464d0,https://doi.org/10.1002/ece3.70402,semantic_scholar,,"ABSTRACT Wildlife abundance and movement are strongly impacted by landscape heterogeneity, especially in cities which are among the world's most heterogeneous landscapes. Nonetheless, current global land cover maps, which are used as a basis for large‐scale spatial ecological modeling, represent urb" | |
| 106,,Graphical representation of global water models,Hannes Müller Schmied; S. Gosling; Marlo Garnsworthy; Laura Müller; C. Telteu,2025,Geoscientific Model Development,,,,,4,0.000,0.000,10.5194/gmd-18-2409-2025,https://www.semanticscholar.org/paper/ec414f79640cde11533c659c35c1f3d57c2ed60b,,semantic_scholar,,"Abstract. Numerical models are simplified representations of the real world at a finite level of complexity. Global water models are used to simulate the terrestrial part of the global water cycle, and their outputs contribute to the evaluation of important natural and societal issues, including wat" | |
| 107,,Relational Causal Models with Cycles: Representation and Reasoning,Ragib Ahsan; D. Arbour; E. Zheleva,2022,CLEaR,,,,,4,0.000,0.000,,https://www.semanticscholar.org/paper/79cd8ce40e351e554c45316010ccbafb6c7d9634,,semantic_scholar,,"Causal reasoning in relational domains is fundamental to studying real-world social phenomena in which individual units can influence each other’s traits and behavior. Dynamics between interconnected units can be represented as an instantiation of a relational causal model; however, causal reasoning " | |
| 108,,Deep Representation Debiasing via Mutual Information Minimization and Maximization (Student Abstract),Ruijiang Han; Wei Wang; Yuxi Long; J. Peng,2022,AAAI Conference on Artificial Intelligence,,,,,1,0.000,0.000,10.1609/aaai.v36i11.21619,https://www.semanticscholar.org/paper/052cbf08c62ee03c5cef3a1e8d929be607454491,https://ojs.aaai.org/index.php/AAAI/article/download/21619/21368,semantic_scholar,,"Deep representation learning has succeeded in several fields. However, pre-trained deep representations are usually biased and make downstream models sensitive to different attributes. In this work, we propose a post-processing unsupervised deep representation debiasing algorithm, DeepMinMax, which " | |
| 109,,UNCERTAINTY REPRESENTATION AND QUANTIFICATION OF 3D BUILDING MODELS,Q. Zou; Monika Sester,2022,"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences",,,,,3,0.000,0.000,10.5194/isprs-archives-xliii-b2-2022-335-2022,https://www.semanticscholar.org/paper/b13bf26b9f77595c6de3f0e418e27e45448a132e,https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B2-2022/335/2022/isprs-archives-XLIII-B2-2022-335-2022.pdf,semantic_scholar,,"Abstract. The quality of environmental perception is of great interest for localization tasks in autonomous systems. Maps, generated from the sensed information, are often used as additional spatial references in these applications. The quantification of the map uncertainties gives an insight into h" | |
| 110,,Compact Abstract Graphs for Detecting Code Vulnerability with GNN Models,Yuxuan Luo; Weifeng Xu; Dianxiang Xu,2022,Asia-Pacific Computer Systems Architecture Conference,,,,,19,0.000,0.000,10.1145/3564625.3564655,https://www.semanticscholar.org/paper/32469361091deb75ee738377e84175e0db3f5b28,,semantic_scholar,, | |
| 111,,Abstract 4366996: Forecasting the Onset of Atrial Fibrillation in the Intensive Care Unit Using Real-World Telemetry Data,D. Howarth; Raghavan Murugan; Robert Parker; V. Herasevich; Kianoush B. Kashani,2025,Circulation,,,,,0,0.000,0.000,10.1161/circ.152.suppl_3.4366996,https://www.semanticscholar.org/paper/425a82174cfb58edb1bedb962804708645335c3c,,semantic_scholar,," | |
| Background: | |
| Paroxysmal atrial fibrillation (AF) is a common arrhythmogenic complication in the intensive care unit (ICU) associated with significant costs and morbidity. AF detection models in the literature have utilized entropic analyses of telemetry to diagnose AF, but they have not been utili" | |
| 112,,Impact of improved representation of volatile organic compound emissions and production of NOx reservoirs on modeled urban ozone production,K. Travis; B. Nault; James H. Crawford; Kelvin H. Bates; Donald R. Blake,2024,Atmospheric Chemistry and Physics,,,,,8,0.000,0.000,10.5194/acp-24-9555-2024,https://www.semanticscholar.org/paper/57ea03a44508592faa6087d446ce34b63229ea99,,semantic_scholar,,"Abstract. The fraction of urban volatile organic compound (VOC) emissions attributable to fossil fuel combustion has been declining in many parts of the world, resulting in a need to better constrain other anthropogenic sources of these emissions. During the National Institute of Environmental Resea" | |
| 113,,Delta-band Activity Underlies Referential Meaning Representation during Pronoun Resolution,Rong Ding; S. T. Oever; Andrea E. Martin,2024,Journal of Cognitive Neuroscience,,,,,2,0.000,0.000,10.1162/jocn_a_02163,https://www.semanticscholar.org/paper/84bc6ba8a5522bb114f9e2ffdc3a41b89b456c91,,semantic_scholar,,"Abstract Human language offers a variety of ways to create meaning, one of which is referring to entities, objects, or events in the world. One such meaning maker is understanding to whom or to what a pronoun in a discourse refers to. To understand a pronoun, the brain must access matching entities " | |
| 114,,Dense Captioning Using Abstract Meaning Representation,A. M. S. A. Neto; Helena de Medeiros Caseli; Tiago A. Almeida,2020,Brazilian Conference on Intelligent Systems,,,,,3,0.000,0.000,10.1007/978-3-030-61377-8_31,https://www.semanticscholar.org/paper/bc7cc5f9e93d59fa6281c2299b65b1021fa0f0b4,,semantic_scholar,, | |
| 115,,Towards precision cosmology with Voids x CMB correlations (I): Roman-Agora mock catalogs and pipeline validation,Mar Pérez Sar; Carlos Hernández Monteagudo; András Kovács; Alice Pisani,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25040v1,https://arxiv.org/pdf/2512.25040v1,arxiv,,"We construct and validate a set of multi-purpose mock galaxy catalogs designed to capture, to different degrees of accuracy, the main characteristics of the Nancy Grace Roman Space Telescope survey. These catalogs provide a foundation for void statistics and various CMB cross-correlation analyses. O" | |
| 116,,MAP: Low-compute Model Merging with Amortized Pareto Fronts via Quadratic Approximation,Lu Li; Tianyu Zhang; Zhiqi Bu; Suyuchen Wang; Huan He,2024,International Conference on Learning Representations,,,,,10,0.000,0.000,10.48550/arXiv.2406.07529,https://www.semanticscholar.org/paper/c51c550cc3dbac1222799a7397bf4d26daf35a7a,,semantic_scholar,,Model merging has emerged as an effective approach to combine multiple single-task models into a multitask model. This process typically involves computing a weighted average of the model parameters without any additional training. Existing model-merging methods focus on enhancing average task accur | |
| 117,,Low-Bandwidth and Compute-Bound RGB-D Planar Semantic SLAM,Jincheng Zhang; P. Ganesh; Kyle Volle; Andrew R. Willis; K. Brink,2021,Italian National Conference on Sensors,,,,,6,0.000,0.000,10.3390/s21165400,https://www.semanticscholar.org/paper/1a5f9a24e7ad365f45ba104c06204b8d7560ccc9,https://www.mdpi.com/1424-8220/21/16/5400/pdf,semantic_scholar,,"Visual simultaneous location and mapping (SLAM) using RGB-D cameras has been a necessary capability for intelligent mobile robots. However, when using point-cloud map representations as most RGB-D SLAM systems do, limitations in onboard compute resources, and especially communication bandwidth can s" | |
| 118,,A Lightweight Low-Power Model for the Detection of Plant Leaf Diseases,Uday Chandra; ·. Abhishek; Lava Bhargava; Jagannath Nanda; Rajendra Prasad Yadav,2024,SN Computer Science,,,,,7,0.000,0.000,10.1007/s42979-024-02658-y,https://www.semanticscholar.org/paper/0bc9ffa722e19b0c3498f009dc9943594aed6087,,semantic_scholar,, | |
| 119,,Skeleton Tracking Solutions for a Low-Cost Stroke Rehabilitation Support System,A. Cóias; Min Hun Lee; Alexandre Bernardino; A. Smailagic,2023,International Conference on Rehabilitation Robotics,,,,,5,0.000,0.000,10.1109/ICORR58425.2023.10304749,https://www.semanticscholar.org/paper/1d3d02bea2df5ce75877ea8217f2bedb931052b8,,semantic_scholar,,"Computer systems based on motion assessment are promising solutions to support stroke survivors' autonomous rehabilitation exercises. In this regard, researchers keep trying to achieve engaging and low-cost solutions suitable mainly for home use. Aiming to achieve a system with a minimal technical s" | |
| 120,,"Furcifer: a Context Adaptive Middleware for Real-world Object Detection Exploiting Local, Edge, and Split Computing in the Cloud Continuum",Matteo Mendula; Paolo Bellavista; Marco Levorato; Sharon L. G. Contreras,2024,Annual IEEE International Conference on Pervasive Computing and Communications,,,,,7,0.000,0.000,10.1109/PerCom59722.2024.10494426,https://www.semanticscholar.org/paper/91ebf13471dc22e2fa69dc4229ec3d981dfafedc,,semantic_scholar,,Modern real-time applications widely embed compute intense neural algorithms at their core. Current solutions to support such algorithms either deploy highly-optimized Deep Neural Networks at mobile devices or offload the execution of possibly larger higher-performance neural models to edge servers. | |
| 121,,A Survey of Methods for Low-Power Deep Learning and Computer Vision,Abhinav Goel; Caleb Tung; Yung-Hsiang Lu; G. Thiruvathukal,2020,World Forum on Internet of Things,,,,,100,0.000,0.000,10.1109/WF-IoT48130.2020.9221198,https://www.semanticscholar.org/paper/0ac0c18fdb61577b5e67c5eb23cf8e1113dac0ba,https://arxiv.org/pdf/2003.11066,semantic_scholar,,"Deep neural networks (DNNs) are successful in many computer vision tasks. However, the most accurate DNNs require millions of parameters and operations, making them energy, computation and memory intensive. This impedes the deployment of large DNNs in low-power devices with limited compute resources" | |
| 122,,Leapfrogging Medical AI in Low-Resource Contexts Using Edge Tensor Processing Unit,Priyanshu Sinha; J. Gichoya; S. Purkayastha,2022,2022 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT),,,,,8,0.000,0.000,10.1109/HI-POCT54491.2022.9744071,https://www.semanticscholar.org/paper/a3ec8d93139cffacd9321c4c953736cf493dd1cf,,semantic_scholar,,"With each passing year, the state-of-the-art deep learning neural networks grow larger in size, requiring larger computing and power resources. The high compute resources required by these large networks are alienating the majority of the world population that lives in low-resource settings and lack" | |
| 123,,R2E-Gym: Procedural Environments and Hybrid Verifiers for Scaling Open-Weights SWE Agents,Naman Jain; Jaskirat Singh; Manish Shetty; Liang Zheng; Koushik Sen,2025,arXiv.org,,,,,33,0.000,0.000,10.48550/arXiv.2504.07164,https://www.semanticscholar.org/paper/5c29ce07433679fc5e2a5bed83d3a80464b966e6,,semantic_scholar,,"Improving open-source models on real-world SWE tasks (solving GITHUB issues) faces two key challenges: 1) scalable curation of execution environments to train these models, and, 2) optimal scaling of test-time compute. We introduce AgentGym, the largest procedurally-curated executable gym environmen" | |
| 124,,Approximating the λ-low-density Value,Joachim Gudmundsson; Zijin Huang; Sampson Wong,2022,International Computing and Combinatorics Conference,,,,,1,0.000,0.000,10.1007/978-3-031-49190-0_5,https://www.semanticscholar.org/paper/9e4671d83ccec393a535fa07074e36a21f1bed01,,semantic_scholar,,"The use of realistic input models has gained popularity in the theory community. Assuming a realistic input model often precludes complicated hypothetical inputs, and the analysis yields bounds that better reflect the behaviour of algorithms in practice. One of the most popular models for polygonal " | |
| 125,,Projected Changes in the Southern Indian Ocean Cyclone Activity Assessed from High-Resolution Experiments and CMIP5 Models,J. Cattiaux; F. Chauvin; O. Bousquet; S. Malardel; Chia‐Lun Tsai,2020,Journal of Climate,,,,,23,0.000,0.000,10.1175/jcli-d-19-0591.1,https://www.semanticscholar.org/paper/cf2cbc0813dcae6f604b9d0c5dc3b56ed3159c83,https://hal.archives-ouvertes.fr/hal-02516828/file/Cattiaux2020JClim.pdf,semantic_scholar,,"The evolution of tropical cyclone activity under climate change remains a crucial scientific issue. Physical theory of cyclogenesis is limited, observational datasets suffer from heterogeneities in space and time, and state-of-the-art climate models used for future projections are still too coarse (" | |
| 126,,Rethinking Generalization in American Sign Language Prediction for Edge Devices with Extremely Low Memory Footprint,A. Paul; P. Mohan; Stuti Sehgal,2020,IEEE Recent Advances in Intelligent Computational Systems,,,,,21,0.000,0.000,10.1109/RAICS51191.2020.9332480,https://www.semanticscholar.org/paper/5fe02f9d41b21b2340365104f44a4279db12af57,https://arxiv.org/pdf/2011.13741,semantic_scholar,,"Due to the boom in technical compute in the last few years, the world has seen massive advances in artificially intelligent systems solving diverse real-world problems. But a major roadblock in the ubiquitous acceptance of these models is their enormous computational complexity and memory footprint." | |
| 127,,Targeting Hypertension Screening in Low‐ and Middle‐Income Countries: A Cross‐Sectional Analysis of 1.2 Million Adults in 56 Countries,Tabea K. Kirschbaum; M. Theilmann; N. Sudharsanan; J. Manne‐Goehler; J. Lemp,2021,Journal of the American Heart Association : Cardiovascular and Cerebrovascular Disease,,,,,5,0.000,0.000,10.1161/JAHA.121.021063,https://www.semanticscholar.org/paper/98510cfca9793a3197fbe6ba9174751f1d5a764f,https://www.ahajournals.org/doi/pdf/10.1161/JAHA.121.021063,semantic_scholar,,"Background As screening programs in low‐ and middle‐income countries (LMICs) often do not have the resources to screen the entire population, there is frequently a need to target such efforts to easily identifiable priority groups. This study aimed to determine (1) how hypertension prevalence in LMI" | |
| 128,,Data-Driven Estimation of Li-Ion Battery Health: Integrating GPT-4 With Distilled Lifelong Learning,Wesley Qi Tong Poh; Yan Xu; Robert Thiam Poh Tan,2025,IEEE transactions on energy conversion,,,,,3,0.000,0.000,10.1109/TEC.2025.3548400,https://www.semanticscholar.org/paper/590f4c071a51c6c1edf1c779599fbfab949c5d17,,semantic_scholar,,"Data-driven methods have attracted significant interests to estimate the state-of-health (SOH) of lithium-ion batteries (LIBs). Yet, it is often laborious and computationally costly to robustly train and implement one machine learning model for different LIB chemistries across varied operations. In " | |
| 129,,Position: The Future of Bayesian Prediction Is Prior-Fitted,Samuel Muller; Arik Reuter; Noah Hollmann; David Rugamer; Frank Hutter,2025,arXiv.org,,,,,4,0.000,0.000,10.48550/arXiv.2505.23947,https://www.semanticscholar.org/paper/232be86fc232e2756debe6eaed8e375537588911,,semantic_scholar,,Training neural networks on randomly generated artificial datasets yields Bayesian models that capture the prior defined by the dataset-generating distribution. Prior-data Fitted Networks (PFNs) are a class of methods designed to leverage this insight. In an era of rapidly increasing computational r | |
| 130,,Vision-centric Token Compression in Large Language Model,Ling Xing; Alex Jinpeng Wang; Rui Yan; Jinhui Tang,2025,arXiv.org,,,,,3,0.000,0.000,10.48550/arXiv.2502.00791,https://www.semanticscholar.org/paper/50b73ac7a0819180aba30d2059e341db5caa87fa,,semantic_scholar,,"Real-world applications are stretching context windows to hundreds of thousand of tokens while Large Language Models (LLMs) swell from billions to trillions of parameters. This dual expansion send compute and memory costs skyrocketing, making token compression indispensable. We introduce Vision Cent" | |
| 131,,Polar Sparsity: High Throughput Batched LLM Inferencing with Scalable Contextual Sparsity,Susav Shrestha; Brad Settlemyer; Nikoli Dryden; Narasimha Reddy,2025,arXiv.org,,,,,3,0.000,0.000,10.48550/arXiv.2505.14884,https://www.semanticscholar.org/paper/328fcf757fa9647ea5eb96d37ee735e52a43df43,,semantic_scholar,,"Accelerating large language model (LLM) inference is critical for real-world deployments requiring high throughput and low latency. Contextual sparsity, where each token dynamically activates only a small subset of the model parameters, shows promise but does not scale to large batch sizes due to un" | |
| 132,,Identifying Sparsely Active Circuits Through Local Loss Landscape Decomposition,B. Chrisman; Lucius Bushnaq; Lee Sharkey,2025,arXiv.org,,,,,2,0.000,0.000,10.48550/arXiv.2504.00194,https://www.semanticscholar.org/paper/bf4b53cceb8beba152b79d946997180363898829,,semantic_scholar,,"Much of mechanistic interpretability has focused on understanding the activation spaces of large neural networks. However, activation space-based approaches reveal little about the underlying circuitry used to compute features. To better understand the circuits employed by models, we introduce a new" | |
| 133,,LUNE: Efficient LLM Unlearning via LoRA Fine-Tuning with Negative Examples,Yezi Liu; Hanning Chen; Wenjun Huang; Yang Ni; Mohsen Imani,2025,,,,,,2,0.000,0.000,,https://www.semanticscholar.org/paper/78081516f448d8113a321f109fe6164fa4ae4baa,,semantic_scholar,,"Large language models (LLMs) possess vast knowledge acquired from extensive training corpora, but they often cannot remove specific pieces of information when needed, which makes it hard to handle privacy, bias mitigation, and knowledge correction. Traditional model unlearning approaches require com" | |
| 134,,OCCAM: Towards Cost-Efficient and Accuracy-Aware Image Classification Inference,Dujian Ding; Bicheng Xu; L. Lakshmanan,2024,International Conference on Learning Representations,,,,,3,0.000,0.000,10.48550/arXiv.2406.04508,https://www.semanticscholar.org/paper/43b839deec89604bb21e888d994bbf15889b921d,,semantic_scholar,,"Classification tasks play a fundamental role in various applications, spanning domains such as healthcare, natural language processing and computer vision. With the growing popularity and capacity of machine learning models, people can easily access trained classifiers as a service online or offline" | |