Unnamed: 0
int64
0
217
id
int64
1,526,373,200B
1,546,707,910B
tweet_text
stringlengths
76
140
paper_reference
stringlengths
20
113
like_count
int64
8
2.72k
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
YAML Metadata Warning: empty or missing yaml metadata in repo card (https://huggingface.co/docs/hub/datasets-cards)

This dataset contains Twitter information from AK92501

Downloads last month
1
Edit dataset card