CShorten/Tweets-from-AK · Datasets at Hugging Face

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

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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

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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

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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

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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

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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

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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

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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

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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

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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?

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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?

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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

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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?

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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1,539,777,865,688,010,800

reStructured Pre-training abs: https://t.co/mYm7qbt59N https://t.co/O5T3tSY4PL

reStructured Pre-training

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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

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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

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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

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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

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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

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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

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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!

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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

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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

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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?

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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1,537,288,570,880,368,600

Masked Siamese ConvNets abs: https://t.co/YMG1O1ZZ5N https://t.co/LCVqVvFNfR

Masked Siamese ConvNets

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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