date timestamp[s]date 2023-05-05 00:00:00 2026-09-18 00:00:00 | arxiv_id stringlengths 10 10 | title stringlengths 6 202 | authors listlengths 1 3.3k | github stringlengths 0 132 | project_page stringlengths 0 130 | abstract stringlengths 165 1.92k | github_manual stringclasses 615
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2023-05-26T00:00:00 | 2305.15719 | Efficient Neural Music Generation | [
"Max W. Y. Lam",
"Qiao Tian",
"Tang Li",
"Zongyu Yin",
"Siyuan Feng",
"Ming Tu",
"Yuliang Ji",
"Rui Xia",
"Mingbo Ma",
"Xuchen Song",
"Jitong Chen",
"Yuping Wang",
"Yuxuan Wang"
] | Recent progress in music generation has been remarkably advanced by the state-of-the-art MusicLM, which comprises a hierarchy of three LMs, respectively, for semantic, coarse acoustic, and fine acoustic modelings. Yet, sampling with the MusicLM requires processing through these LMs one by one to obtain the fine-grained... | |||||||
2023-05-26T00:00:00 | 2305.15586 | Manifold Diffusion Fields | [
"Ahmed A. Elhag",
"Joshua M. Susskind",
"Miguel Angel Bautista"
] | We present Manifold Diffusion Fields (MDF), an approach to learn generative models of continuous functions defined over Riemannian manifolds. Leveraging insights from spectral geometry analysis, we define an intrinsic coordinate system on the manifold via the eigen-functions of the Laplace-Beltrami Operator. MDF repres... | |||||||
2023-05-26T00:00:00 | 2305.15581 | Unsupervised Semantic Correspondence Using Stable Diffusion | [
"Eric Hedlin",
"Gopal Sharma",
"Shweta Mahajan",
"Hossam Isack",
"Abhishek Kar",
"Andrea Tagliasacchi",
"Kwang Moo Yi"
] | https://github.com/ubc-vision/LDM_correspondences | Text-to-image diffusion models are now capable of generating images that are often indistinguishable from real images. To generate such images, these models must understand the semantics of the objects they are asked to generate. In this work we show that, without any training, one can leverage this semantic knowledge ... | https://github.com/ubc-vision/LDM_correspondences | auto | ||||
2023-05-26T00:00:00 | 2305.15717 | The False Promise of Imitating Proprietary LLMs | [
"Arnav Gudibande",
"Eric Wallace",
"Charlie Snell",
"Xinyang Geng",
"Hao Liu",
"Pieter Abbeel",
"Sergey Levine",
"Dawn Song"
] | https://github.com/young-geng/EasyLM | An emerging method to cheaply improve a weaker language model is to finetune it on outputs from a stronger model, such as a proprietary system like ChatGPT (e.g., Alpaca, Self-Instruct, and others). This approach looks to cheaply imitate the proprietary model's capabilities using a weaker open-source model. In this wor... | https://github.com/young-geng/EasyLM | |||||
2023-05-26T00:00:00 | 2305.15486 | SPRING: GPT-4 Out-performs RL Algorithms by Studying Papers and Reasoning | [
"Yue Wu",
"So Yeon Min",
"Shrimai Prabhumoye",
"Yonatan Bisk",
"Ruslan Salakhutdinov",
"Amos Azaria",
"Tom Mitchell",
"Yuanzhi Li"
] | https://github.com/holmeswww/spring | Open-world survival games pose significant challenges for AI algorithms due to their multi-tasking, deep exploration, and goal prioritization requirements. Despite reinforcement learning (RL) being popular for solving games, its high sample complexity limits its effectiveness in complex open-world games like Crafter or... | https://github.com/holmeswww/spring | |||||
2023-05-29T00:00:00 | 2305.16765 | Backpack Language Models | [
"John Hewitt",
"John Thickstun",
"Christopher D. Manning",
"Percy Liang"
] | We present Backpacks: a new neural architecture that marries strong modeling performance with an interface for interpretability and control. Backpacks learn multiple non-contextual sense vectors for each word in a vocabulary, and represent a word in a sequence as a context-dependent, non-negative linear combination of ... | |||||||
2023-05-29T00:00:00 | 2305.16355 | PandaGPT: One Model To Instruction-Follow Them All | [
"Yixuan Su",
"Tian Lan",
"Huayang Li",
"Jialu Xu",
"Yan Wang",
"Deng Cai"
] | https://github.com/yxuansu/pandagpt | We present PandaGPT, an approach to emPower large lANguage moDels with visual and Auditory instruction-following capabilities. Our pilot experiments show that PandaGPT can perform complex tasks such as detailed image description generation, writing stories inspired by videos, and answering questions about audios. More ... | https://github.com/yxuansu/pandagpt | auto | ||||
2023-05-29T00:00:00 | 2305.16338 | Think Before You Act: Decision Transformers with Internal Working Memory | [
"Jikun Kang",
"Romain Laroche",
"Xindi Yuan",
"Adam Trischler",
"Xue Liu",
"Jie Fu"
] | https://github.com/luciferkonn/dt_mem | Large language model (LLM)-based decision-making agents have shown the ability to generalize across multiple tasks. However, their performance relies on massive data and compute. We argue that this inefficiency stems from the forgetting phenomenon, in which a model memorizes its behaviors in parameters throughout train... | https://github.com/luciferkonn/dt_mem | |||||
2023-05-29T00:00:00 | 2305.17098 | ControlVideo: Adding Conditional Control for One Shot Text-to-Video Editing | [
"Min Zhao",
"Rongzhen Wang",
"Fan Bao",
"Chongxuan Li",
"Jun Zhu"
] | https://github.com/thu-ml/controlvideo | In this paper, we present ControlVideo, a novel method for text-driven video editing. Leveraging the capabilities of text-to-image diffusion models and ControlNet, ControlVideo aims to enhance the fidelity and temporal consistency of videos that align with a given text while preserving the structure of the source video... | https://github.com/thu-ml/controlvideo | |||||
2023-05-29T00:00:00 | 2305.16334 | OlaGPT: Empowering LLMs With Human-like Problem-Solving Abilities | [
"Yuanzhen Xie",
"Tao Xie",
"Mingxiong Lin",
"WenTao Wei",
"Chenglin Li",
"Beibei Kong",
"Lei Chen",
"Chengxiang Zhuo",
"Bo Hu",
"Zang Li"
] | https://github.com/oladata-team/OlaGPT | In most current research, large language models (LLMs) are able to perform reasoning tasks by generating chains of thought through the guidance of specific prompts. However, there still exists a significant discrepancy between their capability in solving complex reasoning problems and that of humans. At present, most a... | https://github.com/oladata-team/OlaGPT | |||||
2023-05-29T00:00:00 | 2305.16311 | Break-A-Scene: Extracting Multiple Concepts from a Single Image | [
"Omri Avrahami",
"Kfir Aberman",
"Ohad Fried",
"Daniel Cohen-Or",
"Dani Lischinski"
] | https://github.com/google/break-a-scene | Text-to-image model personalization aims to introduce a user-provided concept to the model, allowing its synthesis in diverse contexts. However, current methods primarily focus on the case of learning a single concept from multiple images with variations in backgrounds and poses, and struggle when adapted to a differen... | https://github.com/google/break-a-scene | |||||
2023-05-29T00:00:00 | 2305.16843 | Randomized Positional Encodings Boost Length Generalization of Transformers | [
"Anian Ruoss",
"Grégoire Delétang",
"Tim Genewein",
"Jordi Grau-Moya",
"Róbert Csordás",
"Mehdi Bennani",
"Shane Legg",
"Joel Veness"
] | https://github.com/deepmind/randomized_positional_encodings | Transformers have impressive generalization capabilities on tasks with a fixed context length. However, they fail to generalize to sequences of arbitrary length, even for seemingly simple tasks such as duplicating a string. Moreover, simply training on longer sequences is inefficient due to the quadratic computation co... | https://github.com/deepmind/randomized_positional_encodings | auto | ||||
2023-05-29T00:00:00 | 2305.16704 | A Closer Look at In-Context Learning under Distribution Shifts | [
"Kartik Ahuja",
"David Lopez-Paz"
] | https://github.com/facebookresearch/iclmlp | In-context learning, a capability that enables a model to learn from input examples on the fly without necessitating weight updates, is a defining characteristic of large language models. In this work, we follow the setting proposed in (Garg et al., 2022) to better understand the generality and limitations of in-contex... | https://github.com/facebookresearch/iclmlp | |||||
2023-05-29T00:00:00 | 2305.16380 | Scan and Snap: Understanding Training Dynamics and Token Composition in 1-layer Transformer | [
"Yuandong Tian",
"Yiping Wang",
"Beidi Chen",
"Simon Du"
] | Transformer architecture has shown impressive performance in multiple research domains and has become the backbone of many neural network models. However, there is limited understanding on how it works. In particular, with a simple predictive loss, how the representation emerges from the gradient training dynamics rema... | |||||||
2023-05-29T00:00:00 | 2305.16999 | Three Towers: Flexible Contrastive Learning with Pretrained Image Models | [
"Jannik Kossen",
"Mark Collier",
"Basil Mustafa",
"Xiao Wang",
"Xiaohua Zhai",
"Lucas Beyer",
"Andreas Steiner",
"Jesse Berent",
"Rodolphe Jenatton",
"Efi Kokiopoulou"
] | We introduce Three Towers (3T), a flexible method to improve the contrastive learning of vision-language models by incorporating pretrained image classifiers. While contrastive models are usually trained from scratch, LiT (Zhai et al., 2022) has recently shown performance gains from using pretrained classifier embeddin... | |||||||
2023-05-29T00:00:00 | 2305.16960 | Training Socially Aligned Language Models in Simulated Human Society | [
"Ruibo Liu",
"Ruixin Yang",
"Chenyan Jia",
"Ge Zhang",
"Denny Zhou",
"Andrew M. Dai",
"Diyi Yang",
"Soroush Vosoughi"
] | Social alignment in AI systems aims to ensure that these models behave according to established societal values. However, unlike humans, who derive consensus on value judgments through social interaction, current language models (LMs) are trained to rigidly replicate their training corpus in isolation, leading to subpa... | |||||||
2023-05-29T00:00:00 | 2305.16958 | MixCE: Training Autoregressive Language Models by Mixing Forward and Reverse Cross-Entropies | [
"Shiyue Zhang",
"Shijie Wu",
"Ozan Irsoy",
"Steven Lu",
"Mohit Bansal",
"Mark Dredze",
"David Rosenberg"
] | https://github.com/bloomberg/mixce-acl2023 | Autoregressive language models are trained by minimizing the cross-entropy of the model distribution Q relative to the data distribution P -- that is, minimizing the forward cross-entropy, which is equivalent to maximum likelihood estimation (MLE). We have observed that models trained in this way may "over-generalize",... | https://github.com/bloomberg/mixce-acl2023 | |||||
2023-05-29T00:00:00 | 2305.16806 | Do GPTs Produce Less Literal Translations? | [
"Vikas Raunak",
"Arul Menezes",
"Matt Post",
"Hany Hassan Awadallah"
] | https://github.com/vyraun/literalness | Large Language Models (LLMs) such as GPT-3 have emerged as general-purpose language models capable of addressing many natural language generation or understanding tasks. On the task of Machine Translation (MT), multiple works have investigated few-shot prompting mechanisms to elicit better translations from LLMs. Howev... | https://github.com/vyraun/literalness | auto | ||||
2023-05-29T00:00:00 | 2305.16635 | Impossible Distillation: from Low-Quality Model to High-Quality Dataset & Model for Summarization and Paraphrasing | [
"Jaehun Jung",
"Peter West",
"Liwei Jiang",
"Faeze Brahman",
"Ximing Lu",
"Jillian Fisher",
"Taylor Sorensen",
"Yejin Choi"
] | It is commonly perceived that the strongest language models (LMs) rely on a combination of massive scale, instruction data, and human feedback to perform specialized tasks -- e.g. summarization and paraphrasing, without supervision. In this paper, we propose that language models can learn to summarize and paraphrase se... | |||||||
2023-05-29T00:00:00 | 2305.16367 | Role-Play with Large Language Models | [
"Murray Shanahan",
"Kyle McDonell",
"Laria Reynolds"
] | As dialogue agents become increasingly human-like in their performance, it is imperative that we develop effective ways to describe their behaviour in high-level terms without falling into the trap of anthropomorphism. In this paper, we foreground the concept of role-play. Casting dialogue agent behaviour in terms of r... | |||||||
2023-05-29T00:00:00 | 2305.16349 | Lexinvariant Language Models | [
"Qian Huang",
"Eric Zelikman",
"Sarah Li Chen",
"Yuhuai Wu",
"Gregory Valiant",
"Percy Liang"
] | Token embeddings, a mapping from discrete lexical symbols to continuous vectors, are at the heart of any language model (LM). However, lexical symbol meanings can also be determined and even redefined by their structural role in a long context. In this paper, we ask: is it possible for a language model to be performant... | |||||||
2023-05-29T00:00:00 | 2305.17066 | Mindstorms in Natural Language-Based Societies of Mind | [
"Mingchen Zhuge",
"Haozhe Liu",
"Francesco Faccio",
"Dylan R. Ashley",
"Róbert Csordás",
"Anand Gopalakrishnan",
"Abdullah Hamdi",
"Hasan Abed Al Kader Hammoud",
"Vincent Herrmann",
"Kazuki Irie",
"Louis Kirsch",
"Bing Li",
"Guohao Li",
"Shuming Liu",
"Jinjie Mai",
"Piotr Piękos",
"A... | Both Minsky's "society of mind" and Schmidhuber's "learning to think" inspire diverse societies of large multimodal neural networks (NNs) that solve problems by interviewing each other in a "mindstorm." Recent implementations of NN-based societies of minds consist of large language models (LLMs) and other NN-based expe... | |||||||
2023-05-29T00:00:00 | 2305.16411 | ZeroAvatar: Zero-shot 3D Avatar Generation from a Single Image | [
"Zhenzhen Weng",
"Zeyu Wang",
"Serena Yeung"
] | Recent advancements in text-to-image generation have enabled significant progress in zero-shot 3D shape generation. This is achieved by score distillation, a methodology that uses pre-trained text-to-image diffusion models to optimize the parameters of a 3D neural presentation, e.g. Neural Radiance Field (NeRF). While ... | |||||||
2023-05-29T00:00:00 | 2305.16381 | DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models | [
"Ying Fan",
"Olivia Watkins",
"Yuqing Du",
"Hao Liu",
"Moonkyung Ryu",
"Craig Boutilier",
"Pieter Abbeel",
"Mohammad Ghavamzadeh",
"Kangwook Lee",
"Kimin Lee"
] | https://github.com/THUDM/ImageReward/blob/main/data/test.json | Learning from human feedback has been shown to improve text-to-image models. These techniques first learn a reward function that captures what humans care about in the task and then improve the models based on the learned reward function. Even though relatively simple approaches (e.g., rejection sampling based on rewar... | https://github.com/THUDM/ImageReward/blob/main/data/test.json | |||||
2023-05-29T00:00:00 | 2305.17126 | Large Language Models as Tool Makers | [
"Tianle Cai",
"Xuezhi Wang",
"Tengyu Ma",
"Xinyun Chen",
"Denny Zhou"
] | Recent research shows the potential of enhancing the problem-solving ability of large language models (LLMs) through the use of external tools. However, prior work along this line depends on the availability of existing tools. In this work, we take an initial step towards removing this dependency by proposing a closed-... | |||||||
2023-05-29T00:00:00 | 2305.16867 | Playing repeated games with Large Language Models | [
"Elif Akata",
"Lion Schulz",
"Julian Coda-Forno",
"Seong Joon Oh",
"Matthias Bethge",
"Eric Schulz"
] | Large Language Models (LLMs) are transforming society and permeating into diverse applications. As a result, LLMs will frequently interact with us and other agents. It is, therefore, of great societal value to understand how LLMs behave in interactive social settings. Here, we propose to use behavioral game theory to s... | |||||||
2023-05-30T00:00:00 | 2305.18295 | RAPHAEL: Text-to-Image Generation via Large Mixture of Diffusion Paths | [
"Zeyue Xue",
"Guanglu Song",
"Qiushan Guo",
"Boxiao Liu",
"Zhuofan Zong",
"Yu Liu",
"Ping Luo"
] | Text-to-image generation has recently witnessed remarkable achievements. We introduce a text-conditional image diffusion model, termed RAPHAEL, to generate highly artistic images, which accurately portray the text prompts, encompassing multiple nouns, adjectives, and verbs. This is achieved by stacking tens of mixture-... | |||||||
2023-05-30T00:00:00 | 2305.17216 | Generating Images with Multimodal Language Models | [
"Jing Yu Koh",
"Daniel Fried",
"Ruslan Salakhutdinov"
] | https://github.com/kohjingyu/gill | We propose a method to fuse frozen text-only large language models (LLMs) with pre-trained image encoder and decoder models, by mapping between their embedding spaces. Our model demonstrates a wide suite of multimodal capabilities: image retrieval, novel image generation, and multimodal dialogue. Ours is the first appr... | https://github.com/kohjingyu/gill | |||||
2023-05-30T00:00:00 | 2305.18274 | Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priors | [
"Paul S. Scotti",
"Atmadeep Banerjee",
"Jimmie Goode",
"Stepan Shabalin",
"Alex Nguyen",
"Ethan Cohen",
"Aidan J. Dempster",
"Nathalie Verlinde",
"Elad Yundler",
"David Weisberg",
"Kenneth A. Norman",
"Tanishq Mathew Abraham"
] | https://github.com/medarc-ai/fmri-reconstruction-nsd | We present MindEye, a novel fMRI-to-image approach to retrieve and reconstruct viewed images from brain activity. Our model comprises two parallel submodules that are specialized for retrieval (using contrastive learning) and reconstruction (using a diffusion prior). MindEye can map fMRI brain activity to any high dime... | https://github.com/medarc-ai/fmri-reconstruction-nsd | auto | ||||
2023-05-30T00:00:00 | 2305.17144 | Ghost in the Minecraft: Generally Capable Agents for Open-World Enviroments via Large Language Models with Text-based Knowledge and Memory | [
"Xizhou Zhu",
"Yuntao Chen",
"Hao Tian",
"Chenxin Tao",
"Weijie Su",
"Chenyu Yang",
"Gao Huang",
"Bin Li",
"Lewei Lu",
"Xiaogang Wang",
"Yu Qiao",
"Zhaoxiang Zhang",
"Jifeng Dai"
] | https://github.com/OpenGVLab/GITM | The captivating realm of Minecraft has attracted substantial research interest in recent years, serving as a rich platform for developing intelligent agents capable of functioning in open-world environments. However, the current research landscape predominantly focuses on specific objectives, such as the popular "Obtai... | https://github.com/OpenGVLab/GITM | |||||
2023-05-30T00:00:00 | 2305.17306 | Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models' Reasoning Performance | [
"Yao Fu",
"Litu Ou",
"Mingyu Chen",
"Yuhao Wan",
"Hao Peng",
"Tushar Khot"
] | https://github.com/franxyao/chain-of-thought-hub | As large language models (LLMs) are continuously being developed, their evaluation becomes increasingly important yet challenging. This work proposes Chain-of-Thought Hub, an open-source evaluation suite on the multi-step reasoning capabilities of large language models. We are interested in this setting for two reasons... | https://github.com/franxyao/chain-of-thought-hub | auto | ||||
2023-05-30T00:00:00 | 2305.17333 | Fine-Tuning Language Models with Just Forward Passes | [
"Sadhika Malladi",
"Tianyu Gao",
"Eshaan Nichani",
"Alex Damian",
"Jason D. Lee",
"Danqi Chen",
"Sanjeev Arora"
] | https://github.com/princeton-nlp/mezo | Fine-tuning language models (LMs) has yielded success on diverse downstream tasks, but as LMs grow in size, backpropagation requires a prohibitively large amount of memory. Zeroth-order (ZO) methods can in principle estimate gradients using only two forward passes but are theorized to be catastrophically slow for optim... | https://github.com/princeton-nlp/mezo | auto | ||||
2023-05-30T00:00:00 | 2305.18264 | Gen-L-Video: Multi-Text to Long Video Generation via Temporal Co-Denoising | [
"Fu-Yun Wang",
"Wenshuo Chen",
"Guanglu Song",
"Han-Jia Ye",
"Yu Liu",
"Hongsheng Li"
] | https://github.com/G-U-N/Gen-L-Video | Leveraging large-scale image-text datasets and advancements in diffusion models, text-driven generative models have made remarkable strides in the field of image generation and editing. This study explores the potential of extending the text-driven ability to the generation and editing of multi-text conditioned long vi... | https://github.com/G-U-N/Gen-L-Video | |||||
2023-05-30T00:00:00 | 2305.18247 | TaleCrafter: Interactive Story Visualization with Multiple Characters | [
"Yuan Gong",
"Youxin Pang",
"Xiaodong Cun",
"Menghan Xia",
"Haoxin Chen",
"Longyue Wang",
"Yong Zhang",
"Xintao Wang",
"Ying Shan",
"Yujiu Yang"
] | https://github.com/videocrafter/talecrafter | Accurate Story visualization requires several necessary elements, such as identity consistency across frames, the alignment between plain text and visual content, and a reasonable layout of objects in images. Most previous works endeavor to meet these requirements by fitting a text-to-image (T2I) model on a set of vide... | https://github.com/videocrafter/talecrafter | auto | ||||
2023-05-30T00:00:00 | 2305.18231 | High-Fidelity Image Compression with Score-based Generative Models | [
"Emiel Hoogeboom",
"Eirikur Agustsson",
"Fabian Mentzer",
"Luca Versari",
"George Toderici",
"Lucas Theis"
] | Despite the tremendous success of diffusion generative models in text-to-image generation, replicating this success in the domain of image compression has proven difficult. In this paper, we demonstrate that diffusion can significantly improve perceptual quality at a given bit-rate, outperforming state-of-the-art appro... | |||||||
2023-05-30T00:00:00 | 2305.18292 | Mix-of-Show: Decentralized Low-Rank Adaptation for Multi-Concept Customization of Diffusion Models | [
"Yuchao Gu",
"Xintao Wang",
"Jay Zhangjie Wu",
"Yujun Shi",
"Yunpeng Chen",
"Zihan Fan",
"Wuyou Xiao",
"Rui Zhao",
"Shuning Chang",
"Weijia Wu",
"Yixiao Ge",
"Ying Shan",
"Mike Zheng Shou"
] | https://github.com/TencentARC/Mix-of-Show | Public large-scale text-to-image diffusion models, such as Stable Diffusion, have gained significant attention from the community. These models can be easily customized for new concepts using low-rank adaptations (LoRAs). However, the utilization of multiple concept LoRAs to jointly support multiple customized concepts... | https://github.com/TencentARC/Mix-of-Show | auto | ||||
2023-05-30T00:00:00 | 2305.18286 | Photoswap: Personalized Subject Swapping in Images | [
"Jing Gu",
"Yilin Wang",
"Nanxuan Zhao",
"Tsu-Jui Fu",
"Wei Xiong",
"Qing Liu",
"Zhifei Zhang",
"He Zhang",
"Jianming Zhang",
"HyunJoon Jung",
"Xin Eric Wang"
] | In an era where images and visual content dominate our digital landscape, the ability to manipulate and personalize these images has become a necessity. Envision seamlessly substituting a tabby cat lounging on a sunlit window sill in a photograph with your own playful puppy, all while preserving the original charm and ... | |||||||
2023-05-30T00:00:00 | 2305.18259 | GlyphControl: Glyph Conditional Control for Visual Text Generation | [
"Yukang Yang",
"Dongnan Gui",
"Yuhui Yuan",
"Haisong Ding",
"Han Hu",
"Kai Chen"
] | https://github.com/aigtext/glyphcontrol-release | Recently, there has been a growing interest in developing diffusion-based text-to-image generative models capable of generating coherent and well-formed visual text. In this paper, we propose a novel and efficient approach called GlyphControl to address this task. Unlike existing methods that rely on character-aware te... | https://github.com/aigtext/glyphcontrol-release | auto | ||||
2023-05-30T00:00:00 | 2305.18098 | BigTrans: Augmenting Large Language Models with Multilingual Translation Capability over 100 Languages | [
"Wen Yang",
"Chong Li",
"Jiajun Zhang",
"Chengqing Zong"
] | Large language models (LLMs) demonstrate promising translation performance among various natural languages. However, many LLMs especially the open-sourced ones, such as BLOOM and LLaMA, are English-dominant and support only dozens of natural languages, making the potential of LLMs on language translation less explored.... | |||||||
2023-05-30T00:00:00 | 2305.17493 | Model Dementia: Generated Data Makes Models Forget | [
"Ilia Shumailov",
"Zakhar Shumaylov",
"Yiren Zhao",
"Yarin Gal",
"Nicolas Papernot",
"Ross Anderson"
] | Stable Diffusion revolutionised image creation from descriptive text. GPT-2, GPT-3(.5) and GPT-4 demonstrated astonishing performance across a variety of language tasks. ChatGPT introduced such language models to the general public. It is now clear that large language models (LLMs) are here to stay, and will bring abou... | |||||||
2023-05-30T00:00:00 | 2305.17390 | SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks | [
"Bill Yuchen Lin",
"Yicheng Fu",
"Karina Yang",
"Prithviraj Ammanabrolu",
"Faeze Brahman",
"Shiyu Huang",
"Chandra Bhagavatula",
"Yejin Choi",
"Xiang Ren"
] | We introduce SwiftSage, a novel agent framework inspired by the dual-process theory of human cognition, designed to excel in action planning for complex interactive reasoning tasks. SwiftSage integrates the strengths of behavior cloning and prompting large language models (LLMs) to enhance task completion performance. ... | |||||||
2023-05-30T00:00:00 | 2305.17359 | DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of GPT-Generated Text | [
"Xianjun Yang",
"Wei Cheng",
"Linda Petzold",
"William Yang Wang",
"Haifeng Chen"
] | https://github.com/Xianjun-Yang/DNA-GPT | Large language models (LLMs) have notably enhanced the fluency and diversity of machine-generated text. However, this progress also presents a significant challenge in detecting the origin of a given text, and current research on detection methods lags behind the rapid evolution of LLMs. Conventional training-based met... | https://github.com/Xianjun-Yang/DNA-GPT | |||||
2023-05-31T00:00:00 | 2305.19164 | LANCE: Stress-testing Visual Models by Generating Language-guided Counterfactual Images | [
"Viraj Prabhu",
"Sriram Yenamandra",
"Prithvijit Chattopadhyay",
"Judy Hoffman"
] | https://github.com/virajprabhu/lance | We propose an automated algorithm to stress-test a trained visual model by generating language-guided counterfactual test images (LANCE). Our method leverages recent progress in large language modeling and text-based image editing to augment an IID test set with a suite of diverse, realistic, and challenging test image... | https://github.com/virajprabhu/lance | auto | ||||
2023-05-31T00:00:00 | 2305.19012 | StyleAvatar3D: Leveraging Image-Text Diffusion Models for High-Fidelity 3D Avatar Generation | [
"Chi Zhang",
"Yiwen Chen",
"Yijun Fu",
"Zhenglin Zhou",
"Gang YU",
"Billzb Wang",
"Bin Fu",
"Tao Chen",
"Guosheng Lin",
"Chunhua Shen"
] | https://github.com/icoz69/styleavatar3d | The recent advancements in image-text diffusion models have stimulated research interest in large-scale 3D generative models. Nevertheless, the limited availability of diverse 3D resources presents significant challenges to learning. In this paper, we present a novel method for generating high-quality, stylized 3D avat... | https://github.com/icoz69/styleavatar3d | |||||
2023-05-31T00:00:00 | 2305.18752 | GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction | [
"Rui Yang",
"Lin Song",
"Yanwei Li",
"Sijie Zhao",
"Yixiao Ge",
"Xiu Li",
"Ying Shan"
] | https://github.com/StevenGrove/GPT4Tools | This paper aims to efficiently enable Large Language Models (LLMs) to use multimodal tools. Advanced proprietary LLMs, such as ChatGPT and GPT-4, have shown great potential for tool usage through sophisticated prompt engineering. Nevertheless, these models typically rely on prohibitive computational costs and publicly ... | https://github.com/StevenGrove/GPT4Tools | |||||
2023-05-31T00:00:00 | 2305.18802 | LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpus | [
"Yuma Koizumi",
"Heiga Zen",
"Shigeki Karita",
"Yifan Ding",
"Kohei Yatabe",
"Nobuyuki Morioka",
"Michiel Bacchiani",
"Yu Zhang",
"Wei Han",
"Ankur Bapna"
] | This paper introduces a new speech dataset called ``LibriTTS-R'' designed for text-to-speech (TTS) use. It is derived by applying speech restoration to the LibriTTS corpus, which consists of 585 hours of speech data at 24 kHz sampling rate from 2,456 speakers and the corresponding texts. The constituent samples of Libr... | |||||||
2023-05-31T00:00:00 | 2305.19245 | AlteredAvatar: Stylizing Dynamic 3D Avatars with Fast Style Adaptation | [
"Thu Nguyen-Phuoc",
"Gabriel Schwartz",
"Yuting Ye",
"Stephen Lombardi",
"Lei Xiao"
] | This paper presents a method that can quickly adapt dynamic 3D avatars to arbitrary text descriptions of novel styles. Among existing approaches for avatar stylization, direct optimization methods can produce excellent results for arbitrary styles but they are unpleasantly slow. Furthermore, they require redoing the op... | |||||||
2023-05-31T00:00:00 | 2305.18415 | Geometric Algebra Transformers | [
"Johann Brehmer",
"Pim de Haan",
"Sönke Behrends",
"Taco Cohen"
] | Problems involving geometric data arise in a variety of fields, including computer vision, robotics, chemistry, and physics. Such data can take numerous forms, such as points, direction vectors, planes, or transformations, but to date there is no single architecture that can be applied to such a wide variety of geometr... | |||||||
2023-05-31T00:00:00 | 2305.19234 | Grammar Prompting for Domain-Specific Language Generation with Large Language Models | [
"Bailin Wang",
"Zi Wang",
"Xuezhi Wang",
"Yuan Cao",
"Rif A. Saurous",
"Yoon Kim"
] | https://github.com/berlino/grammar-prompting | Large language models (LLMs) can learn to perform a wide range of natural language tasks from just a handful of in-context examples. However, for generating strings from highly structured languages (e.g., semantic parsing to complex domain-specific languages), it is challenging for the LLM to generalize from just a few... | https://github.com/berlino/grammar-prompting | |||||
2023-05-31T00:00:00 | 2305.18654 | Faith and Fate: Limits of Transformers on Compositionality | [
"Nouha Dziri",
"Ximing Lu",
"Melanie Sclar",
"Xiang Lorraine Li",
"Liwei Jian",
"Bill Yuchen Lin",
"Peter West",
"Chandra Bhagavatula",
"Ronan Le Bras",
"Jena D. Hwang",
"Soumya Sanyal",
"Sean Welleck",
"Xiang Ren",
"Allyson Ettinger",
"Zaid Harchaoui",
"Yejin Choi"
] | https://github.com/nouhadziri/faith-and-fate | Transformer large language models (LLMs) have sparked admiration for their exceptional performance on tasks that demand intricate multi-step reasoning. Yet, these models simultaneously show failures on surprisingly trivial problems. This begs the question: Are these errors incidental, or do they signal more substantial... | https://github.com/nouhadziri/faith-and-fate | |||||
2023-05-31T00:00:00 | 2305.18565 | PaLI-X: On Scaling up a Multilingual Vision and Language Model | [
"Xi Chen",
"Josip Djolonga",
"Piotr Padlewski",
"Basil Mustafa",
"Soravit Changpinyo",
"Jialin Wu",
"Carlos Riquelme Ruiz",
"Sebastian Goodman",
"Xiao Wang",
"Yi Tay",
"Siamak Shakeri",
"Mostafa Dehghani",
"Daniel Salz",
"Mario Lucic",
"Michael Tschannen",
"Arsha Nagrani",
"Hexiang H... | We present the training recipe and results of scaling up PaLI-X, a multilingual vision and language model, both in terms of size of the components and the breadth of its training task mixture. Our model achieves new levels of performance on a wide-range of varied and complex tasks, including multiple image-based captio... | |||||||
2023-05-31T00:00:00 | 2305.18373 | KAFA: Rethinking Image Ad Understanding with Knowledge-Augmented Feature Adaptation of Vision-Language Models | [
"Zhiwei Jia",
"Pradyumna Narayana",
"Arjun R. Akula",
"Garima Pruthi",
"Hao Su",
"Sugato Basu",
"Varun Jampani"
] | Image ad understanding is a crucial task with wide real-world applications. Although highly challenging with the involvement of diverse atypical scenes, real-world entities, and reasoning over scene-texts, how to interpret image ads is relatively under-explored, especially in the era of foundational vision-language mod... | |||||||
2023-05-31T00:00:00 | 2305.18766 | HiFA: High-fidelity Text-to-3D with Advanced Diffusion Guidance | [
"Joseph Zhu",
"Peiye Zhuang"
] | https://github.com/JunzheJosephZhu/HiFA | Automatic text-to-3D synthesis has achieved remarkable advancements through the optimization of 3D models. Existing methods commonly rely on pre-trained text-to-image generative models, such as diffusion models, providing scores for 2D renderings of Neural Radiance Fields (NeRFs) and being utilized for optimizing NeRFs... | https://github.com/JunzheJosephZhu/HiFA | auto | ||||
2023-05-31T00:00:00 | 2305.18474 | Make-An-Audio 2: Temporal-Enhanced Text-to-Audio Generation | [
"Jiawei Huang",
"Yi Ren",
"Rongjie Huang",
"Dongchao Yang",
"Zhenhui Ye",
"Chen Zhang",
"Jinglin Liu",
"Xiang Yin",
"Zejun Ma",
"Zhou Zhao"
] | https://github.com/bytedance/make-an-audio-2 | Large diffusion models have been successful in text-to-audio (T2A) synthesis tasks, but they often suffer from common issues such as semantic misalignment and poor temporal consistency due to limited natural language understanding and data scarcity. Additionally, 2D spatial structures widely used in T2A works lead to u... | https://github.com/bytedance/make-an-audio-2 | auto | ||||
2023-05-31T00:00:00 | 2305.19066 | Nested Diffusion Processes for Anytime Image Generation | [
"Noam Elata",
"Bahjat Kawar",
"Tomer Michaeli",
"Michael Elad"
] | https://github.com/noamelata/nesteddiffusion | Diffusion models are the current state-of-the-art in image generation, synthesizing high-quality images by breaking down the generation process into many fine-grained denoising steps. Despite their good performance, diffusion models are computationally expensive, requiring many neural function evaluations (NFEs). In th... | https://github.com/noamelata/nesteddiffusion | |||||
2023-05-31T00:00:00 | 2305.18729 | Real-World Image Variation by Aligning Diffusion Inversion Chain | [
"Yuechen Zhang",
"Jinbo Xing",
"Eric Lo",
"Jiaya Jia"
] | https://github.com/dvlab-research/rival | Recent diffusion model advancements have enabled high-fidelity images to be generated using text prompts. However, a domain gap exists between generated images and real-world images, which poses a challenge in generating high-quality variations of real-world images. Our investigation uncovers that this domain gap origi... | https://github.com/dvlab-research/rival | auto | ||||
2023-05-31T00:00:00 | 2305.18583 | Controllable Text-to-Image Generation with GPT-4 | [
"Tianjun Zhang",
"Yi Zhang",
"Vibhav Vineet",
"Neel Joshi",
"Xin Wang"
] | Current text-to-image generation models often struggle to follow textual instructions, especially the ones requiring spatial reasoning. On the other hand, Large Language Models (LLMs), such as GPT-4, have shown remarkable precision in generating code snippets for sketching out text inputs graphically, e.g., via TikZ. I... | |||||||
2023-05-31T00:00:00 | 2305.18365 | What indeed can GPT models do in chemistry? A comprehensive benchmark on eight tasks | [
"Taicheng Guo",
"Kehan Guo",
"Bozhao nan",
"Zhengwen Liang",
"Zhichun Guo",
"Nitesh V. Chawla",
"Olaf Wiest",
"Xiangliang Zhang"
] | https://github.com/chemfoundationmodels/chemllmbench | Large Language Models (LLMs) with strong abilities in natural language processing tasks have emerged and have been rapidly applied in various kinds of areas such as science, finance and software engineering. However, the capability of LLMs to advance the field of chemistry remains unclear. In this paper,we establish a ... | https://github.com/chemfoundationmodels/chemllmbench | |||||
2023-06-01T00:00:00 | 2305.20030 | Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust | [
"Yuxin Wen",
"John Kirchenbauer",
"Jonas Geiping",
"Tom Goldstein"
] | https://github.com/YuxinWenRick/tree-ring-watermark | Watermarking the outputs of generative models is a crucial technique for tracing copyright and preventing potential harm from AI-generated content. In this paper, we introduce a novel technique called Tree-Ring Watermarking that robustly fingerprints diffusion model outputs. Unlike existing methods that perform post-ho... | https://github.com/YuxinWenRick/tree-ring-watermark | |||||
2023-06-01T00:00:00 | 2305.20081 | Efficient Diffusion Policies for Offline Reinforcement Learning | [
"Bingyi Kang",
"Xiao Ma",
"Chao Du",
"Tianyu Pang",
"Shuicheng Yan"
] | https://github.com/sail-sg/edp | Offline reinforcement learning (RL) aims to learn optimal policies from offline datasets, where the parameterization of policies is crucial but often overlooked. Recently, Diffsuion-QL significantly boosts the performance of offline RL by representing a policy with a diffusion model, whose success relies on a parametri... | https://github.com/sail-sg/edp | |||||
2023-06-01T00:00:00 | 2305.20091 | Humans in 4D: Reconstructing and Tracking Humans with Transformers | [
"Shubham Goel",
"Georgios Pavlakos",
"Jathushan Rajasegaran",
"Angjoo Kanazawa",
"Jitendra Malik"
] | https://github.com/shubham-goel/4D-Humans | We present an approach to reconstruct humans and track them over time. At the core of our approach, we propose a fully "transformerized" version of a network for human mesh recovery. This network, HMR 2.0, advances the state of the art and shows the capability to analyze unusual poses that have in the past been difficu... | https://github.com/shubham-goel/4D-Humans | auto | ||||
2023-06-01T00:00:00 | 2305.19452 | Bigger, Better, Faster: Human-level Atari with human-level efficiency | [
"Max Schwarzer",
"Johan Obando-Ceron",
"Aaron Courville",
"Marc Bellemare",
"Rishabh Agarwal",
"Pablo Samuel Castro"
] | https://github.com/google-research/google-research/tree/master/bigger_better_faster | We introduce a value-based RL agent, which we call BBF, that achieves super-human performance in the Atari 100K benchmark. BBF relies on scaling the neural networks used for value estimation, as well as a number of other design choices that enable this scaling in a sample-efficient manner. We conduct extensive analyses... | https://github.com/google-research/google-research/tree/master/bigger_better_faster | |||||
2023-06-01T00:00:00 | 2305.20088 | Improving CLIP Training with Language Rewrites | [
"Lijie Fan",
"Dilip Krishnan",
"Phillip Isola",
"Dina Katabi",
"Yonglong Tian"
] | https://github.com/LijieFan/LaCLIP | Contrastive Language-Image Pre-training (CLIP) stands as one of the most effective and scalable methods for training transferable vision models using paired image and text data. CLIP models are trained using contrastive loss, which typically relies on data augmentations to prevent overfitting and shortcuts. However, in... | https://github.com/LijieFan/LaCLIP | |||||
2023-06-01T00:00:00 | 2305.20010 | Human or Not? A Gamified Approach to the Turing Test | [
"Daniel Jannai",
"Amos Meron",
"Barak Lenz",
"Yoav Levine",
"Yoav Shoham"
] | We present "Human or Not?", an online game inspired by the Turing test, that measures the capability of AI chatbots to mimic humans in dialog, and of humans to tell bots from other humans. Over the course of a month, the game was played by over 1.5 million users who engaged in anonymous two-minute chat sessions with ei... | |||||||
2023-06-01T00:00:00 | 2305.19835 | Deliberate then Generate: Enhanced Prompting Framework for Text Generation | [
"Bei Li",
"Rui Wang",
"Junliang Guo",
"Kaitao Song",
"Xu Tan",
"Hany Hassan",
"Arul Menezes",
"Tong Xiao",
"Jiang Bian",
"JingBo Zhu"
] | Large language models (LLMs) have shown remarkable success across a wide range of natural language generation tasks, where proper prompt designs make great impacts. While existing prompting methods are normally restricted to providing correct information, in this paper, we encourage the model to deliberate by proposing... | |||||||
2023-06-01T00:00:00 | 2305.19472 | PlaSma: Making Small Language Models Better Procedural Knowledge Models for (Counterfactual) Planning | [
"Faeze Brahman",
"Chandra Bhagavatula",
"Valentina Pyatkin",
"Jena D. Hwang",
"Xiang Lorraine Li",
"Hirona J. Arai",
"Soumya Sanyal",
"Keisuke Sakaguchi",
"Xiang Ren",
"Yejin Choi"
] | https://github.com/allenai/plasma | Procedural planning, which entails decomposing a high-level goal into a sequence of temporally ordered steps, is an important yet intricate task for machines. It involves integrating common-sense knowledge to reason about complex contextualized situations that are often counterfactual, e.g. "scheduling a doctor's appoi... | https://github.com/allenai/plasma | |||||
2023-06-01T00:00:00 | 2305.19370 | Blockwise Parallel Transformer for Long Context Large Models | [
"Hao Liu",
"Pieter Abbeel"
] | Transformers have emerged as the cornerstone of state-of-the-art natural language processing models, showcasing exceptional performance across a wide range of AI applications. However, the memory demands posed by the self-attention mechanism and the large feedforward network in Transformers limit their ability to handl... | |||||||
2023-06-01T00:00:00 | 2305.20086 | Understanding and Mitigating Copying in Diffusion Models | [
"Gowthami Somepalli",
"Vasu Singla",
"Micah Goldblum",
"Jonas Geiping",
"Tom Goldstein"
] | https://github.com/somepago/dcr | Images generated by diffusion models like Stable Diffusion are increasingly widespread. Recent works and even lawsuits have shown that these models are prone to replicating their training data, unbeknownst to the user. In this paper, we first analyze this memorization problem in text-to-image diffusion models. While it... | https://github.com/somepago/dcr | |||||
2023-06-01T00:00:00 | 2305.20082 | Control4D: Dynamic Portrait Editing by Learning 4D GAN from 2D Diffusion-based Editor | [
"Ruizhi Shao",
"Jingxiang Sun",
"Cheng Peng",
"Zerong Zheng",
"Boyao Zhou",
"Hongwen Zhang",
"Yebin Liu"
] | Recent years have witnessed considerable achievements in editing images with text instructions. When applying these editors to dynamic scene editing, the new-style scene tends to be temporally inconsistent due to the frame-by-frame nature of these 2D editors. To tackle this issue, we propose Control4D, a novel approach... | |||||||
2023-06-02T00:00:00 | 2306.00980 | SnapFusion: Text-to-Image Diffusion Model on Mobile Devices within Two Seconds | [
"Yanyu Li",
"Huan Wang",
"Qing Jin",
"Ju Hu",
"Pavlo Chemerys",
"Yun Fu",
"Yanzhi Wang",
"Sergey Tulyakov",
"Jian Ren"
] | Text-to-image diffusion models can create stunning images from natural language descriptions that rival the work of professional artists and photographers. However, these models are large, with complex network architectures and tens of denoising iterations, making them computationally expensive and slow to run. As a re... | |||||||
2023-06-02T00:00:00 | 2306.00739 | SQL-PaLM: Improved Large Language ModelAdaptation for Text-to-SQL | [
"Ruoxi Sun",
"Sercan O Arik",
"Hootan Nakhost",
"Hanjun Dai",
"Rajarishi Sinha",
"Pengcheng Yin",
"Tomas Pfister"
] | One impressive emergent capability of large language models (LLMs) is generation of code, including Structured Query Language (SQL) for databases. For the task of converting natural language text to SQL queries, Text-to-SQL, adaptation of LLMs is of paramount importance, both in in-context learning and fine-tuning sett... | |||||||
2023-06-02T00:00:00 | 2306.00378 | Example-based Motion Synthesis via Generative Motion Matching | [
"Weiyu Li",
"Xuelin Chen",
"Peizhuo Li",
"Olga Sorkine-Hornung",
"Baoquan Chen"
] | https://github.com/wyysf-98/GenMM | We present GenMM, a generative model that "mines" as many diverse motions as possible from a single or few example sequences. In stark contrast to existing data-driven methods, which typically require long offline training time, are prone to visual artifacts, and tend to fail on large and complex skeletons, GenMM inher... | https://github.com/wyysf-98/GenMM | auto | ||||
2023-06-02T00:00:00 | 2306.00238 | Bytes Are All You Need: Transformers Operating Directly On File Bytes | [
"Maxwell Horton",
"Sachin Mehta",
"Ali Farhadi",
"Mohammad Rastegari"
] | https://github.com/apple/ml-cvnets/tree/main/examples | Modern deep learning approaches usually transform inputs into a modality-specific form. For example, the most common deep learning approach to image classification involves decoding image file bytes into an RGB tensor which is passed into a neural network. Instead, we investigate performing classification directly on f... | https://github.com/apple/ml-cvnets/tree/main/examples | |||||
2023-06-02T00:00:00 | 2306.00983 | StyleDrop: Text-to-Image Generation in Any Style | [
"Kihyuk Sohn",
"Nataniel Ruiz",
"Kimin Lee",
"Daniel Castro Chin",
"Irina Blok",
"Huiwen Chang",
"Jarred Barber",
"Lu Jiang",
"Glenn Entis",
"Yuanzhen Li",
"Yuan Hao",
"Irfan Essa",
"Michael Rubinstein",
"Dilip Krishnan"
] | Pre-trained large text-to-image models synthesize impressive images with an appropriate use of text prompts. However, ambiguities inherent in natural language and out-of-distribution effects make it hard to synthesize image styles, that leverage a specific design pattern, texture or material. In this paper, we introduc... | |||||||
2023-06-02T00:00:00 | 2306.00890 | LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day | [
"Chunyuan Li",
"Cliff Wong",
"Sheng Zhang",
"Naoto Usuyama",
"Haotian Liu",
"Jianwei Yang",
"Tristan Naumann",
"Hoifung Poon",
"Jianfeng Gao"
] | https://github.com/microsoft/LLaVA-Med | Conversational generative AI has demonstrated remarkable promise for empowering biomedical practitioners, but current investigations focus on unimodal text. Multimodal conversational AI has seen rapid progress by leveraging billions of image-text pairs from the public web, but such general-domain vision-language models... | https://github.com/microsoft/LLaVA-Med | auto | ||||
2023-06-02T00:00:00 | 2306.00637 | Wuerstchen: Efficient Pretraining of Text-to-Image Models | [
"Pablo Pernias",
"Dominic Rampas",
"Marc Aubreville"
] | We introduce Wuerstchen, a novel technique for text-to-image synthesis that unites competitive performance with unprecedented cost-effectiveness and ease of training on constrained hardware. Building on recent advancements in machine learning, our approach, which utilizes latent diffusion strategies at strong latent im... | |||||||
2023-06-02T00:00:00 | 2306.00966 | The Hidden Language of Diffusion Models | [
"Hila Chefer",
"Oran Lang",
"Mor Geva",
"Volodymyr Polosukhin",
"Assaf Shocher",
"Michal Irani",
"Inbar Mosseri",
"Lior Wolf"
] | https://github.com/hila-chefer/Conceptor | Text-to-image diffusion models have demonstrated an unparalleled ability to generate high-quality, diverse images from a textual concept (e.g., "a doctor", "love"). However, the internal process of mapping text to a rich visual representation remains an enigma. In this work, we tackle the challenge of understanding con... | https://github.com/hila-chefer/Conceptor | auto | ||||
2023-06-02T00:00:00 | 2306.00984 | StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners | [
"Yonglong Tian",
"Lijie Fan",
"Phillip Isola",
"Huiwen Chang",
"Dilip Krishnan"
] | https://github.com/google-research/syn-rep-learn | We investigate the potential of learning visual representations using synthetic images generated by text-to-image models. This is a natural question in the light of the excellent performance of such models in generating high-quality images. We consider specifically the Stable Diffusion, one of the leading open source t... | https://github.com/google-research/syn-rep-learn | auto | ||||
2023-06-02T00:00:00 | 2306.00971 | ViCo: Detail-Preserving Visual Condition for Personalized Text-to-Image Generation | [
"Shaozhe Hao",
"Kai Han",
"Shihao Zhao",
"Kwan-Yee K. Wong"
] | https://github.com/haoosz/vico | Personalized text-to-image generation using diffusion models has recently been proposed and attracted lots of attention. Given a handful of images containing a novel concept (e.g., a unique toy), we aim to tune the generative model to capture fine visual details of the novel concept and generate photorealistic images f... | https://github.com/haoosz/vico | auto | ||||
2023-06-02T00:00:00 | 2306.00956 | The ObjectFolder Benchmark: Multisensory Learning with Neural and Real Objects | [
"Ruohan Gao",
"Yiming Dou",
"Hao Li",
"Tanmay Agarwal",
"Jeannette Bohg",
"Yunzhu Li",
"Li Fei-Fei",
"Jiajun Wu"
] | We introduce the ObjectFolder Benchmark, a benchmark suite of 10 tasks for multisensory object-centric learning, centered around object recognition, reconstruction, and manipulation with sight, sound, and touch. We also introduce the ObjectFolder Real dataset, including the multisensory measurements for 100 real-world ... | |||||||
2023-06-02T00:00:00 | 2306.00926 | Inserting Anybody in Diffusion Models via Celeb Basis | [
"Ge Yuan",
"Xiaodong Cun",
"Yong Zhang",
"Maomao Li",
"Chenyang Qi",
"Xintao Wang",
"Ying Shan",
"Huicheng Zheng"
] | https://github.com/ygtxr1997/celebbasis | Exquisite demand exists for customizing the pretrained large text-to-image model, e.g., Stable Diffusion, to generate innovative concepts, such as the users themselves. However, the newly-added concept from previous customization methods often shows weaker combination abilities than the original ones even given several... | https://github.com/ygtxr1997/celebbasis | auto | ||||
2023-06-02T00:00:00 | 2306.00148 | SafeDiffuser: Safe Planning with Diffusion Probabilistic Models | [
"Wei Xiao",
"Tsun-Hsuan Wang",
"Chuang Gan",
"Daniela Rus"
] | Diffusion model-based approaches have shown promise in data-driven planning, but there are no safety guarantees, thus making it hard to be applied for safety-critical applications. To address these challenges, we propose a new method, called SafeDiffuser, to ensure diffusion probabilistic models satisfy specifications ... | |||||||
2023-06-02T00:00:00 | 2306.00008 | Brainformers: Trading Simplicity for Efficiency | [
"Yanqi Zhou",
"Nan Du",
"Yanping Huang",
"Daiyi Peng",
"Chang Lan",
"Da Huang",
"Siamak Shakeri",
"David So",
"Andrew Dai",
"Yifeng Lu",
"Zhifeng Chen",
"Quoc Le",
"Claire Cui",
"James Laundon",
"Jeff Dean"
] | Transformers are central to recent successes in natural language processing and computer vision. Transformers have a mostly uniform backbone where layers alternate between feed-forward and self-attention in order to build a deep network. Here we investigate this design choice and find that more complex blocks that have... | |||||||
2023-06-02T00:00:00 | 2306.00802 | Birth of a Transformer: A Memory Viewpoint | [
"Alberto Bietti",
"Vivien Cabannes",
"Diane Bouchacourt",
"Herve Jegou",
"Leon Bottou"
] | https://github.com/albietz/transformer-birth | Large language models based on transformers have achieved great empirical successes. However, as they are deployed more widely, there is a growing need to better understand their internal mechanisms in order to make them more reliable. These models appear to store vast amounts of knowledge from their training data, and... | https://github.com/albietz/transformer-birth | |||||
2023-06-02T00:00:00 | 2306.00107 | MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training | [
"Yizhi Li",
"Ruibin Yuan",
"Ge Zhang",
"Yinghao Ma",
"Xingran Chen",
"Hanzhi Yin",
"Chenghua Lin",
"Anton Ragni",
"Emmanouil Benetos",
"Norbert Gyenge",
"Roger Dannenberg",
"Ruibo Liu",
"Wenhu Chen",
"Gus Xia",
"Yemin Shi",
"Wenhao Huang",
"Yike Guo",
"Jie Fu"
] | https://github.com/yizhilll/MERT | Self-supervised learning (SSL) has recently emerged as a promising paradigm for training generalisable models on large-scale data in the fields of vision, text, and speech. Although SSL has been proven effective in speech and audio, its application to music audio has yet to be thoroughly explored. This is primarily due... | https://github.com/yizhilll/MERT | |||||
2023-06-02T00:00:00 | 2306.00029 | CodeTF: One-stop Transformer Library for State-of-the-art Code LLM | [
"Nghi D. Q. Bui",
"Hung Le",
"Yue Wang",
"Junnan Li",
"Akhilesh Deepak Gotmare",
"Steven C. H. Hoi"
] | https://github.com/salesforce/codetf | Code intelligence plays a key role in transforming modern software engineering. Recently, deep learning-based models, especially Transformer-based large language models (LLMs), have demonstrated remarkable potential in tackling these tasks by leveraging massive open-source code data and programming language features. H... | https://github.com/salesforce/codetf | |||||
2023-06-02T00:00:00 | 2306.00110 | MuseCoco: Generating Symbolic Music from Text | [
"Peiling Lu",
"Xin Xu",
"Chenfei Kang",
"Botao Yu",
"Chengyi Xing",
"Xu Tan",
"Jiang Bian"
] | Generating music from text descriptions is a user-friendly mode since the text is a relatively easy interface for user engagement. While some approaches utilize texts to control music audio generation, editing musical elements in generated audio is challenging for users. In contrast, symbolic music offers ease of editi... | |||||||
2023-06-02T00:00:00 | 2306.00943 | Make-Your-Video: Customized Video Generation Using Textual and Structural Guidance | [
"Jinbo Xing",
"Menghan Xia",
"Yuxin Liu",
"Yuechen Zhang",
"Yong Zhang",
"Yingqing He",
"Hanyuan Liu",
"Haoxin Chen",
"Xiaodong Cun",
"Xintao Wang",
"Ying Shan",
"Tien-Tsin Wong"
] | Creating a vivid video from the event or scenario in our imagination is a truly fascinating experience. Recent advancements in text-to-video synthesis have unveiled the potential to achieve this with prompts only. While text is convenient in conveying the overall scene context, it may be insufficient to control precise... | |||||||
2023-06-02T00:00:00 | 2306.00986 | Diffusion Self-Guidance for Controllable Image Generation | [
"Dave Epstein",
"Allan Jabri",
"Ben Poole",
"Alexei A. Efros",
"Aleksander Holynski"
] | Large-scale generative models are capable of producing high-quality images from detailed text descriptions. However, many aspects of an image are difficult or impossible to convey through text. We introduce self-guidance, a method that provides greater control over generated images by guiding the internal representatio... | |||||||
2023-06-02T00:00:00 | 2306.00964 | Cocktail: Mixing Multi-Modality Controls for Text-Conditional Image Generation | [
"Minghui Hu",
"Jianbin Zheng",
"Daqing Liu",
"Chuanxia Zheng",
"Chaoyue Wang",
"Dacheng Tao",
"Tat-Jen Cham"
] | Text-conditional diffusion models are able to generate high-fidelity images with diverse contents. However, linguistic representations frequently exhibit ambiguous descriptions of the envisioned objective imagery, requiring the incorporation of additional control signals to bolster the efficacy of text-guided diffusion... | |||||||
2023-06-02T00:00:00 | 2306.00622 | ReviewerGPT? An Exploratory Study on Using Large Language Models for Paper Reviewing | [
"Ryan Liu",
"Nihar B. Shah"
] | https://github.com/niharshah/ReviewerGPT2023 | Given the rapid ascent of large language models (LLMs), we study the question: (How) can large language models help in reviewing of scientific papers or proposals? We first conduct some pilot studies where we find that (i) GPT-4 outperforms other LLMs (Bard, Vicuna, Koala, Alpaca, LLaMa, Dolly, OpenAssistant, StableLM)... | https://github.com/niharshah/ReviewerGPT2023 | |||||
2023-06-05T00:00:00 | 2306.01116 | The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only | [
"Guilherme Penedo",
"Quentin Malartic",
"Daniel Hesslow",
"Ruxandra Cojocaru",
"Alessandro Cappelli",
"Hamza Alobeidli",
"Baptiste Pannier",
"Ebtesam Almazrouei",
"Julien Launay"
] | Large language models are commonly trained on a mixture of filtered web data and curated high-quality corpora, such as social media conversations, books, or technical papers. This curation process is believed to be necessary to produce performant models with broad zero-shot generalization abilities. However, as larger ... | |||||||
2023-06-05T00:00:00 | 2306.01567 | Segment Anything in High Quality | [
"Lei Ke",
"Mingqiao Ye",
"Martin Danelljan",
"Yifan Liu",
"Yu-Wing Tai",
"Chi-Keung Tang",
"Fisher Yu"
] | https://github.com/SysCV/SAM-HQ | The recent Segment Anything Model (SAM) represents a big leap in scaling up segmentation models, allowing for powerful zero-shot capabilities and flexible prompting. Despite being trained with 1.1 billion masks, SAM's mask prediction quality falls short in many cases, particularly when dealing with objects that have in... | https://github.com/SysCV/SAM-HQ | |||||
2023-06-05T00:00:00 | 2306.01693 | Fine-Grained Human Feedback Gives Better Rewards for Language Model Training | [
"Zeqiu Wu",
"Yushi Hu",
"Weijia Shi",
"Nouha Dziri",
"Alane Suhr",
"Prithviraj Ammanabrolu",
"Noah A. Smith",
"Mari Ostendorf",
"Hannaneh Hajishirzi"
] | https://github.com/allenai/FineGrainedRLHF | Language models (LMs) often exhibit undesirable text generation behaviors, including generating false, toxic, or irrelevant outputs. Reinforcement learning from human feedback (RLHF) - where human preference judgments on LM outputs are transformed into a learning signal - has recently shown promise in addressing these ... | https://github.com/allenai/FineGrainedRLHF | auto | ||||
2023-06-05T00:00:00 | 2306.01694 | Evaluating Language Models for Mathematics through Interactions | [
"Katherine M. Collins",
"Albert Q. Jiang",
"Simon Frieder",
"Lionel Wong",
"Miri Zilka",
"Umang Bhatt",
"Thomas Lukasiewicz",
"Yuhuai Wu",
"Joshua B. Tenenbaum",
"William Hart",
"Timothy Gowers",
"Wenda Li",
"Adrian Weller",
"Mateja Jamnik"
] | https://github.com/collinskatie/checkmate | The standard methodology of evaluating large language models (LLMs) based on static pairs of inputs and outputs is insufficient for developing assistants: this kind of assessments fails to take into account the essential interactive element in their deployment, and therefore limits how we understand language model capa... | https://github.com/collinskatie/checkmate | |||||
2023-06-05T00:00:00 | 2306.01736 | DaTaSeg: Taming a Universal Multi-Dataset Multi-Task Segmentation Model | [
"Xiuye Gu",
"Yin Cui",
"Jonathan Huang",
"Abdullah Rashwan",
"Xuan Yang",
"Xingyi Zhou",
"Golnaz Ghiasi",
"Weicheng Kuo",
"Huizhong Chen",
"Liang-Chieh Chen",
"David A Ross"
] | Observing the close relationship among panoptic, semantic and instance segmentation tasks, we propose to train a universal multi-dataset multi-task segmentation model: DaTaSeg.We use a shared representation (mask proposals with class predictions) for all tasks. To tackle task discrepancy, we adopt different merge opera... | |||||||
2023-06-05T00:00:00 | 2306.01684 | Harnessing large-language models to generate private synthetic text | [
"Alexey Kurakin",
"Natalia Ponomareva",
"Umar Syed",
"Liam MacDermed",
"Andreas Terzis"
] | Differentially private (DP) training methods like DP-SGD can protect sensitive training data by ensuring that ML models will not reveal private information. An alternative approach, which this paper studies, is to use a sensitive dataset to generate a new synthetic dataset which is differentially private with respect t... | |||||||
2023-06-05T00:00:00 | 2306.01242 | Responsible Task Automation: Empowering Large Language Models as Responsible Task Automators | [
"Zhizheng Zhang",
"Xiaoyi Zhang",
"Wenxuan Xie",
"Yan Lu"
] | The recent success of Large Language Models (LLMs) signifies an impressive stride towards artificial general intelligence. They have shown a promising prospect in automatically completing tasks upon user instructions, functioning as brain-like coordinators. The associated risks will be revealed as we delegate an increa... | |||||||
2023-06-05T00:00:00 | 2306.01160 | Faster Causal Attention Over Large Sequences Through Sparse Flash Attention | [
"Matteo Pagliardini",
"Daniele Paliotta",
"Martin Jaggi",
"François Fleuret"
] | https://github.com/epfml/dynamic-sparse-flash-attention | Transformer-based language models have found many diverse applications requiring them to process sequences of increasing length. For these applications, the causal self-attention -- which is the only component scaling quadratically w.r.t. the sequence length -- becomes a central concern. While many works have proposed ... | https://github.com/epfml/dynamic-sparse-flash-attention | |||||
2023-06-05T00:00:00 | 2306.01061 | Reimagining Retrieval Augmented Language Models for Answering Queries | [
"Wang-Chiew Tan",
"Yuliang Li",
"Pedro Rodriguez",
"Richard James",
"Xi Victoria Lin",
"Alon Halevy",
"Scott Yih"
] | We present a reality check on large language models and inspect the promise of retrieval augmented language models in comparison. Such language models are semi-parametric, where models integrate model parameters and knowledge from external data sources to make their predictions, as opposed to the parametric nature of v... |
Subsets and Splits
Top 20 Papers with GitHub Links
This query retrieves 20 entries from the training set where the GitHub URL is not null or empty, providing a basic filter of non-empty entries.