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0
1,546,707,909,748,342,800
"High-resource Language-specific Training for Multilingual Neural Machine Translation abs: https://t.co/fYrwIPVpV2 https://t.co/b23EVZ6J5O"
"High-resource Language-specific Training for Multilingual Neural Machine Translation"
11
1
1,546,669,556,789,387,300
"Exploring Length Generalization in Large Language Models abs: https://t.co/7Gphb7Q8jJ https://t.co/cCpLTSrXfR"
"Exploring Length Generalization in Large Language Models"
17
2
1,546,667,351,885,729,800
"LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action abs:… https://t.co/lCk3P8KIwM"
"LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action"
32
3
1,546,665,636,734,140,400
"Scaling the Number of Tasks in Continual Learning abs: https://t.co/F4HxAxGUpI https://t.co/cyvXSBKthk"
"Scaling the Number of Tasks in Continual Learning"
47
4
1,546,707,909,748,342,800
"High-resource Language-specific Training for Multilingual Neural Machine Translation abs: https://t.co/fYrwIPVpV2 https://t.co/b23EVZ6J5O"
"High-resource Language-specific Training for Multilingual Neural Machine Translation"
11
5
1,546,669,556,789,387,300
"Exploring Length Generalization in Large Language Models abs: https://t.co/7Gphb7Q8jJ https://t.co/cCpLTSrXfR"
"Exploring Length Generalization in Large Language Models"
17
6
1,546,667,351,885,729,800
"LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action abs:… https://t.co/lCk3P8KIwM"
"LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action"
32
7
1,546,665,636,734,140,400
"Scaling the Number of Tasks in Continual Learning abs: https://t.co/F4HxAxGUpI https://t.co/cyvXSBKthk"
"Scaling the Number of Tasks in Continual Learning"
47
8
1,546,379,163,803,721,700
"CausalAgents: A Robustness Benchmark for Motion Forecasting using Causal Relationships abs: https://t.co/ozIrQ7gx68 https://t.co/gSGfnsZbji"
"CausalAgents: A Robustness Benchmark for Motion Forecasting using Causal Relationships"
53
9
1,546,376,106,122,567,700
"The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications a… https://t.co/TOPpVPQbM8"
"The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications"
11
10
1,546,375,104,262,725,600
"Code Translation with Compiler Representations abs: https://t.co/nTT3dmXH4c method improves upon the state of the… https://t.co/wD4SozbilN"
"Code Translation with Compiler Representations"
127
11
1,546,363,822,121,820,200
"End-to-End Binaural Speech Synthesis abs: https://t.co/tR86cSAjQO project page: https://t.co/nB1iSV68U2 end-to-end… https://t.co/OTzfVZTFqb"
"End-to-End Binaural Speech Synthesis"
58
12
1,545,243,820,496,937,000
"Cross-Scale Vector Quantization for Scalable Neural Speech Coding abs: https://t.co/AbE9rP0ApQ https://t.co/pZXUTNipgs"
"Cross-Scale Vector Quantization for Scalable Neural Speech Coding"
25
13
1,545,240,373,328,593,000
"Finding Fallen Objects Via Asynchronous Audio-Visual Integration abs: https://t.co/mv9Rvl0hFA project page:… https://t.co/N8l4zaP9bH"
"Finding Fallen Objects Via Asynchronous Audio-Visual Integration"
33
14
1,545,228,848,391,938,000
"Back to the Source: Diffusion-Driven Test-Time Adaptation abs: https://t.co/5jmESOLQxG https://t.co/cI5UFyQI0B"
"Back to the Source: Diffusion-Driven Test-Time Adaptation"
82
15
1,544,897,525,664,170,000
"When does Bias Transfer in Transfer Learning? abs: https://t.co/tf8FWyf8Ge https://t.co/0l6vy8RHXI"
"When does Bias Transfer in Transfer Learning?"
135
16
1,544,865,587,343,630,300
"Transformers are Adaptable Task Planners abs: https://t.co/6lgFJD2Olt TTP can be pre-trained on multiple preferenc… https://t.co/XrolcxlV22"
"Transformers are Adaptable Task Planners"
82
17
1,544,853,650,316,599,300
"Ultra-Low-Bitrate Speech Coding with Pretrained Transformers abs: https://t.co/rYRe5N7Bqu https://t.co/zOsCY53r2s"
"Ultra-Low-Bitrate Speech Coding with Pretrained Transformers"
34
18
1,544,721,641,049,145,300
"CLEAR: Improving Vision-Language Navigation with Cross-Lingual, Environment-Agnostic Representations abs:… https://t.co/6ng3UArKdE"
"CLEAR: Improving Vision-Language Navigation with Cross-Lingual, Environment-Agnostic Representations"
52
19
1,544,521,037,274,046,500
"An Empirical Study of Implicit Regularization in Deep Offline RL abs: https://t.co/rCjHkQ2jwL https://t.co/8hJOsVA6D0"
"An Empirical Study of Implicit Regularization in Deep Offline RL"
45
20
1,544,519,268,234,154,000
"Offline RL Policies Should be Trained to be Adaptive abs: https://t.co/kC7TPSOTt2 https://t.co/Ox2D028P33"
"Offline RL Policies Should be Trained to be Adaptive"
34
21
1,544,491,557,293,854,700
"Efficient Representation Learning via Adaptive Context Pooling abs: https://t.co/zZzezhvbN7 https://t.co/xJoStGBSqp"
"Efficient Representation Learning via Adaptive Context Pooling"
163
22
1,544,488,616,734,429,200
"CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning abs:… https://t.co/HqXmDpaUEh"
"CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning"
102
23
1,544,485,593,991,811,000
"How Much More Data Do I Need? Estimating Requirements for Downstream Tasks abs: https://t.co/RNXT4IRIaL https://t.co/uJGrEfgaAv"
"How Much More Data Do I Need? Estimating Requirements for Downstream Tasks"
230
24
1,544,483,235,542,990,800
"Neural Networks and the Chomsky Hierarchy abs: https://t.co/u6Jl2WvKMr sota architectures, such as LSTMs and Trans… https://t.co/DyHnH8Q8z7"
"Neural Networks and the Chomsky Hierarchy"
209
25
1,544,207,617,102,332,000
"GlowVC: Mel-spectrogram space disentangling model for language-independent text-free voice conversion abs:… https://t.co/kFYdKhrhSA"
"GlowVC: Mel-spectrogram space disentangling model for language-independent text-free voice conversion"
19
26
1,544,201,186,739,458,000
"Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation abs:… https://t.co/yL9kWlUYfs"
"Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation"
112
27
1,544,193,877,053,161,500
"WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents abs: https://t.co/8hZyMt90Rv pro… https://t.co/eHzGN2GHqj"
"WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents"
52
28
1,544,127,293,660,037,000
"UserLibri: A Dataset for ASR Personalization Using Only Text abs: https://t.co/0bug7OWU42 https://t.co/OMqJSGlqDx"
"UserLibri: A Dataset for ASR Personalization Using Only Text"
9
29
1,543,981,460,964,708,400
"LaserMix for Semi-Supervised LiDAR Semantic Segmentation abs: https://t.co/SvqHy1y7LI project page:… https://t.co/jbQtQiDbDy"
"LaserMix for Semi-Supervised LiDAR Semantic Segmentation"
74
30
1,543,766,808,309,670,000
"Rethinking Optimization with Differentiable Simulation from a Global Perspective abs: https://t.co/trEcw4VZb2 proje… https://t.co/1UsI0q03IL"
"Rethinking Optimization with Differentiable Simulation from a Global Perspective"
94
31
1,543,763,117,515,182,000
"Visual Pre-training for Navigation: What Can We Learn from Noise? abs: https://t.co/Rn5UGvvMMz github:… https://t.co/eKeMSlBxVx"
"Visual Pre-training for Navigation: What Can We Learn from Noise?"
134
32
1,543,759,817,449,390,000
"DeepSpeed Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale abs:… https://t.co/IbF6IdUDj7"
"DeepSpeed Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale"
120
33
1,543,757,524,356,272,000
"When Does Differentially Private Learning Not Suffer in High Dimensions? abs: https://t.co/yws7BhoBaP https://t.co/bD2Gz6B3GU"
"When Does Differentially Private Learning Not Suffer in High Dimensions?"
28
34
1,542,740,430,084,792,300
"Implicit Neural Spatial Filtering for Multichannel Source Separation in the Waveform Domain abs:… https://t.co/3cNoOlr5SD"
"Implicit Neural Spatial Filtering for Multichannel Source Separation in the Waveform Domain"
31
35
1,542,713,456,268,304,400
"Denoised MDPs: Learning World Models Better Than the World Itself abs: https://t.co/CPwlF0soWZ project page:… https://t.co/5BBwGXYZ2l"
"Denoised MDPs: Learning World Models Better Than the World Itself"
98
36
1,542,712,192,746,782,700
"Forecasting Future World Events with Neural Networks abs: https://t.co/tD8F0ZC1rC github: https://t.co/v8HZgye0ZH… https://t.co/eJaakYSUSw"
"Forecasting Future World Events with Neural Networks"
77
37
1,542,709,853,516,431,400
"Learning Iterative Reasoning through Energy Minimization abs: https://t.co/WDLx1hKPqG project page:… https://t.co/oDEClr0ho1"
"Learning Iterative Reasoning through Energy Minimization"
125
38
1,542,709,029,964,849,200
"Improving the Generalization of Supervised Models abs: https://t.co/3CzEuuxvHt project page: https://t.co/uSjiKvSMN8 https://t.co/ffUkpTL7Ng"
"Improving the Generalization of Supervised Models"
189
39
1,542,325,850,036,752,400
"RegMixup: Mixup as a Regularizer Can Surprisingly Improve Accuracy and Out Distribution Robustness abs:… https://t.co/iFAou98U0X"
"RegMixup: Mixup as a Regularizer Can Surprisingly Improve Accuracy and Out Distribution Robustness"
172
40
1,542,316,111,743,664,000
"Masked World Models for Visual Control abs: https://t.co/eZx53zuqnm project page: https://t.co/hgZwrV3zO5 Can MAE… https://t.co/UfybFx81uj"
"Masked World Models for Visual Control"
83
41
1,542,313,347,835,732,000
"Beyond neural scaling laws: beating power law scaling via data pruning abs: https://t.co/OFYkTt5b2d https://t.co/7SKXMClaR8"
"Beyond neural scaling laws: beating power law scaling via data pruning"
164
42
1,542,312,585,768,435,700
"3D-Aware Video Generation abs: https://t.co/N64ARXFKMJ project page: https://t.co/5MoGVKqItn https://t.co/uZdLIXWc1P"
"3D-Aware Video Generation"
122
43
1,541,957,148,070,011,000
"DayDreamer: World Models for Physical Robot Learning abs: https://t.co/quyTQGcjEA project page:… https://t.co/DD67NUzgJy"
"DayDreamer: World Models for Physical Robot Learning"
182
44
1,541,948,699,559,006,200
"Long Range Language Modeling via Gated State Spaces abs: https://t.co/HEd2lwlGan https://t.co/tPOHv7dP0T"
"Long Range Language Modeling via Gated State Spaces"
124
45
1,541,945,827,035,332,600
"ProGen2: Exploring the Boundaries of Protein Language Models abs: https://t.co/kelWMlhH8r github:… https://t.co/nzvei5pMJR"
"ProGen2: Exploring the Boundaries of Protein Language Models"
64
46
1,541,626,617,490,837,500
"Multitask vocal burst modeling with ResNets and pre-trained paralinguistic Conformers abs: https://t.co/QZLcoFOeSz https://t.co/315WfiVVRr"
"Multitask vocal burst modeling with ResNets and pre-trained paralinguistic Conformers"
11
47
1,541,599,748,624,351,200
"Programmatic Concept Learning for Human Motion Description and Synthesis abs: https://t.co/uIoxGozwhD project page:… https://t.co/MmCMQouLF7"
"Programmatic Concept Learning for Human Motion Description and Synthesis"
83
48
1,541,592,312,094,101,500
"Prompting Decision Transformer for Few-Shot Policy Generalization abs: https://t.co/bD2f4SjRP6 project page:… https://t.co/ZfAxxx6zCu"
"Prompting Decision Transformer for Few-Shot Policy Generalization"
48
49
1,541,590,513,241,006,000
"Repository-Level Prompt Generation for Large Language Models of Code abs: https://t.co/GG1YHoCQdf github:… https://t.co/Z9fUO4r8sU"
"Repository-Level Prompt Generation for Large Language Models of Code"
56
50
1,541,588,372,631,818,200
"Your Autoregressive Generative Model Can be Better If You Treat It as an Energy-Based One abs:… https://t.co/uJuKxO7XJC"
"Your Autoregressive Generative Model Can be Better If You Treat It as an Energy-Based One"
121
51
1,541,226,747,533,922,300
"PSP: Million-level Protein Sequence Dataset for Protein Structure Prediction abs: https://t.co/yXdFTqRWF3 dataset… https://t.co/ZDNMPI2NVR"
"PSP: Million-level Protein Sequence Dataset for Protein Structure Prediction"
94
52
1,541,219,433,259,176,000
"Megapixel Image Generation with Step-Unrolled Denoising Autoencoders abs: https://t.co/6fX9PseXBT obtain FID score… https://t.co/HPodJ8xzPx"
"Megapixel Image Generation with Step-Unrolled Denoising Autoencoders"
147
53
1,540,184,734,390,706,200
"Walk the Random Walk: Learning to Discover and Reach Goals Without Supervision abs: https://t.co/NO2vzfdYdS https://t.co/WoN73BzgeQ"
"Walk the Random Walk: Learning to Discover and Reach Goals Without Supervision"
66
54
1,540,176,838,017,917,000
"Offline RL for Natural Language Generation with Implicit Language Q Learning abs: https://t.co/wYTtUgdryZ project p… https://t.co/xS8JCODxwP"
"Offline RL for Natural Language Generation with Implicit Language Q Learning"
43
55
1,540,161,095,930,880,000
"MaskViT: Masked Visual Pre-Training for Video Prediction abs: https://t.co/uhMEB6ashb project page:… https://t.co/gbnxrCxUrc"
"MaskViT: Masked Visual Pre-Training for Video Prediction"
147
56
1,540,156,319,923,060,700
"The ArtBench Dataset: Benchmarking Generative Models with Artworks abs: https://t.co/Zzq0A2i5ob github:… https://t.co/SfQlvTLrk3"
"The ArtBench Dataset: Benchmarking Generative Models with Artworks"
200
57
1,539,811,680,359,796,700
"TiCo: Transformation Invariance and Covariance Contrast for Self-Supervised Visual Representation Learning abs:… https://t.co/UArbr7zhRE"
"TiCo: Transformation Invariance and Covariance Contrast for Self-Supervised Visual Representation Learning"
85
58
1,539,794,210,190,155,800
"Jointist: Joint Learning for Multi-instrument Transcription and Its Applications abs: https://t.co/xeuPUBcr01 proje… https://t.co/QmyCioKviJ"
"Jointist: Joint Learning for Multi-instrument Transcription and Its Applications"
18
59
1,539,780,412,297,330,700
"GEMv2: Multilingual NLG Benchmarking in a Single Line of Code abs: https://t.co/pKS5mgoDkG GEMv2 supports 40 docum… https://t.co/qMitHzTlO0"
"GEMv2: Multilingual NLG Benchmarking in a Single Line of Code"
18
60
1,539,777,865,688,010,800
"reStructured Pre-training abs: https://t.co/mYm7qbt59N https://t.co/O5T3tSY4PL"
"reStructured Pre-training"
32
61
1,539,672,920,456,298,500
"Scaling Autoregressive Models for Content-Rich Text-to-Image Generation paper: https://t.co/NKkTeHttLd project page… https://t.co/CcKxsWPmjR"
"Scaling Autoregressive Models for Content-Rich Text-to-Image Generation"
137
62
1,539,480,179,151,712,300
"Intra-Instance VICReg: Bag of Self-Supervised Image Patch Embedding abs: https://t.co/Bq3GUQywPV https://t.co/iLTaoXm0yC"
"Intra-Instance VICReg: Bag of Self-Supervised Image Patch Embedding"
66
63
1,539,460,213,211,910,100
"EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine abs: https://t.co/F4XkHLRxPi github:… https://t.co/JiwSuMdkZH"
"EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine"
34
64
1,539,459,120,667,021,300
"EpiGRAF: Rethinking training of 3D GANs abs: https://t.co/RcY2vQr0NH project page: https://t.co/kuXPKA00bZ https://t.co/CVCsseAS21"
"EpiGRAF: Rethinking training of 3D GANs"
145
65
1,539,453,554,578,055,200
"Unbiased Teacher v2: Semi-supervised Object Detection for Anchor-free and Anchor-based Detectors abs:… https://t.co/noluSxtqzu"
"Unbiased Teacher v2: Semi-supervised Object Detection for Anchor-free and Anchor-based Detectors"
72
66
1,539,435,374,103,220,200
"Global Context Vision Transformers abs: https://t.co/d6go0yv7fu github: https://t.co/rUYFs09ReC On ImageNet-1K dat… https://t.co/HJnw5wclQV"
"Global Context Vision Transformers"
89
67
1,539,421,251,076,247,600
"(Certified!!) Adversarial Robustness for Free! abs: https://t.co/NTU6lioyII show how to achieve sota certified adv… https://t.co/2VW1CDARya"
"(Certified!!) Adversarial Robustness for Free!"
42
68
1,539,076,449,788,997,600
"A Closer Look at Smoothness in Domain Adversarial Training abs: https://t.co/GgKE9695vj github:… https://t.co/33MX6TZhjt"
"A Closer Look at Smoothness in Domain Adversarial Training"
97
69
1,538,710,356,444,471,300
"Fast Finite Width Neural Tangent Kernel abs: https://t.co/iY1lFoYMjA https://t.co/hWzzcCd5OZ"
"Fast Finite Width Neural Tangent Kernel"
23
70
1,538,706,936,211,951,600
"What do navigation agents learn about their environment? abs: https://t.co/eXelV0REgZ github:… https://t.co/TGSzEQ1v1c"
"What do navigation agents learn about their environment?"
37
71
1,538,698,653,493,338,000
"Bootstrapped Transformer for Offline Reinforcement Learning abs: https://t.co/YiEY3uiTgL https://t.co/yle4hPgMmf"
"Bootstrapped Transformer for Offline Reinforcement Learning"
137
72
1,538,695,457,550,921,700
"Bridge-Tower: Building Bridges Between Encoders in Vision-Language Representation Learning abs:… https://t.co/uLQLmf4l3M"
"Bridge-Tower: Building Bridges Between Encoders in Vision-Language Representation Learning"
42
73
1,538,692,524,830,769,200
"MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge abs: https://t.co/etfGL1xnum project pa… https://t.co/Fv1aLuEJSV"
"MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge"
265
74
1,538,687,423,722,541,000
"Lossy Compression with Gaussian Diffusion abs: https://t.co/tw5YiZAN3B implement a proof of concept and find that… https://t.co/4nvLjhIX4e"
"Lossy Compression with Gaussian Diffusion"
102
75
1,538,686,489,491,648,500
"NU-Wave 2: A General Neural Audio Upsampling Model for Various Sampling Rates abs: https://t.co/4S8sBXq6Ko a diffu… https://t.co/xd3eQ0ApQJ"
"NU-Wave 2: A General Neural Audio Upsampling Model for Various Sampling Rates"
87
76
1,538,006,265,363,738,600
"iBoot: Image-bootstrapped Self-Supervised Video Representation Learning abs: https://t.co/dkZUd4QC81 https://t.co/pJFpxd7ckU"
"iBoot: Image-bootstrapped Self-Supervised Video Representation Learning"
73
77
1,538,000,649,933,115,400
"Neural Scene Representation for Locomotion on Structured Terrain abs: https://t.co/68xY622f4w https://t.co/W3wTYp31f6"
"Neural Scene Representation for Locomotion on Structured Terrain"
83
78
1,537,924,151,389,737,000
"Programmatic Concept Learning for Human Motion Description and Synthesis paper: https://t.co/Qemk23gUHX project pag… https://t.co/ImHeYQC5vj"
"Programmatic Concept Learning for Human Motion Description and Synthesis"
60
79
1,537,640,654,968,324,000
"Spatially-Adaptive Multilayer Selection for GAN Inversion and Editing abs: https://t.co/9tpvhXuaRw project page:… https://t.co/XxpZg5PGke"
"Spatially-Adaptive Multilayer Selection for GAN Inversion and Editing"
73
80
1,537,637,590,274,277,400
"MoDi: Unconditional Motion Synthesis from Diverse Data abs: https://t.co/YBV9jSUemo https://t.co/o1uvG18RSk"
"MoDi: Unconditional Motion Synthesis from Diverse Data"
70
81
1,537,630,146,244,518,000
"OmniMAE: Single Model Masked Pretraining on Images and Videos abs: https://t.co/j9a3imUEJ6 single pretrained model… https://t.co/OiR2pY5emm"
"OmniMAE: Single Model Masked Pretraining on Images and Videos"
146
82
1,537,622,879,386,456,000
"SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos abs: https://t.co/0MkpFJiUzM using spars… https://t.co/x1Hvgf13qE"
"SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos"
54
83
1,537,621,348,339,572,700
"BYOL-Explore: Exploration by Bootstrapped Prediction abs: https://t.co/xXQtolzjlP BYOL-Explore achieves superhuman… https://t.co/uZvAbVd1Bb"
"BYOL-Explore: Exploration by Bootstrapped Prediction"
79
84
1,537,618,457,365,303,300
"Know your audience: specializing grounded language models with the game of Dixit abs: https://t.co/T8d5ir8LDQ https://t.co/zSk5oR2F9D"
"Know your audience: specializing grounded language models with the game of Dixit"
39
85
1,537,323,042,380,124,200
"VCT: A Video Compression Transformer abs: https://t.co/llH1L1ooKa presented an elegantly simple transformer-based… https://t.co/ErovCWVDg3"
"VCT: A Video Compression Transformer"
68
86
1,537,314,480,056,672,300
"Contrastive Learning as Goal-Conditioned Reinforcement Learning abs: https://t.co/6dv7PNn0qq project page:… https://t.co/vRSdekL9If"
"Contrastive Learning as Goal-Conditioned Reinforcement Learning"
77
87
1,537,288,570,880,368,600
"Masked Siamese ConvNets abs: https://t.co/YMG1O1ZZ5N https://t.co/LCVqVvFNfR"
"Masked Siamese ConvNets"
83
88
1,537,265,816,609,116,200
"Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone abs: https://t.co/UgdYW9Cf1g project page:… https://t.co/v2sTfFBq5r"
"Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone"
89
89
1,537,257,011,657,814,000
"Variable Bitrate Neural Fields abs: https://t.co/Rp1t2LaQaW project page: https://t.co/e2t8OrznxI https://t.co/6hw7OwbjZN"
"Variable Bitrate Neural Fields"
162
90
1,537,254,679,188,488,200
"A Unified Sequence Interface for Vision Tasks abs: https://t.co/hXbVXdqHh1 explore a unified sequence interface fo… https://t.co/QG5UxvIgS4"
"A Unified Sequence Interface for Vision Tasks"
50
91
1,537,252,952,666,087,400
"Prefix Language Models are Unified Modal Learners abs: https://t.co/BD4b3rQnKg https://t.co/2ofScnMIKN"
"Prefix Language Models are Unified Modal Learners"
66
92
1,537,248,480,074,293,200
"Diffusion Models for Video Prediction and Infilling abs: https://t.co/MwfxwKXG4z project page:… https://t.co/rnwB8eGFAs"
"Diffusion Models for Video Prediction and Infilling"
103
93
1,536,879,515,883,946,000
"ReCo: Retrieve and Co-segment for Zero-shot Transfer abs: https://t.co/YwxkCGGyG1 project page:… https://t.co/WzVhmfhWCz"
"ReCo: Retrieve and Co-segment for Zero-shot Transfer"
58
94
1,536,872,875,885,580,300
"Object Scene Representation Transformer abs: https://t.co/SUfNIBGAxt project page: https://t.co/j8ebSAeM8v scales… https://t.co/wa4vo3RJAK"
"Object Scene Representation Transformer"
97
95
1,536,871,347,372,052,500
"Adversarial Audio Synthesis with Complex-valued Polynomial Networks abs: https://t.co/ekeC0nKIhR APOLLO results in… https://t.co/sDcl2nydkt"
"Adversarial Audio Synthesis with Complex-valued Polynomial Networks"
23
96
1,536,526,888,289,575,000
"Large-Scale Retrieval for Reinforcement Learning abs: https://t.co/fjzGvI3ZXB https://t.co/eFRHt8yXoq"
"Large-Scale Retrieval for Reinforcement Learning"
86
97
1,536,522,198,785,183,700
"GLIPv2: Unifying Localization and Vision-Language Understanding abs: https://t.co/3GomrHG8xq github:… https://t.co/bD68NZk4Lp"
"GLIPv2: Unifying Localization and Vision-Language Understanding"
73
98
1,536,521,362,898,145,300
"Self-critiquing models for assisting human evaluators abs: https://t.co/8Zy2xfA5Qz https://t.co/qndZMS9zXa"
"Self-critiquing models for assisting human evaluators"
19
99
1,536,515,535,202,136,000
"Multi-instrument Music Synthesis with Spectrogram Diffusion abs: https://t.co/UNDV4e7A6R use a simple two-stage pr… https://t.co/AebIraqLF2"
"Multi-instrument Music Synthesis with Spectrogram Diffusion"
87
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