Upload ArXiv-Tweets-from-AK.csv

ArXiv-Tweets-from-AK.csv

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+,id,tweet_text,paper_reference,like_count
+0,1546707909748342784,"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
+1,1546669556789387264,"Exploring Length Generalization in Large Language Models
+abs: https://t.co/7Gphb7Q8jJ https://t.co/cCpLTSrXfR",Exploring Length Generalization in Large Language Models,17
+2,1546667351885729792,"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
+3,1546665636734140417,"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
+4,1546707909748342784,"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
+5,1546669556789387264,"Exploring Length Generalization in Large Language Models
+abs: https://t.co/7Gphb7Q8jJ https://t.co/cCpLTSrXfR",Exploring Length Generalization in Large Language Models,17
+6,1546667351885729792,"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
+7,1546665636734140417,"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
+8,1546379163803721729,"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
+9,1546376106122567681,"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
+10,1546375104262725632,"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
+11,1546363822121820162,"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
+12,1545243820496936960,"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
+13,1545240373328592897,"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
+14,1545228848391938048,"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
+15,1544897525664169986,"When does Bias Transfer in Transfer Learning?
+abs: https://t.co/tf8FWyf8Ge https://t.co/0l6vy8RHXI",When does Bias Transfer in Transfer Learning?,135
+16,1544865587343630342,"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
+17,1544853650316599299,"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
+18,1544721641049145345,"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
+19,1544521037274046464,"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
+20,1544519268234153984,"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
+21,1544491557293854721,"Efficient Representation Learning via Adaptive Context Pooling
+abs: https://t.co/zZzezhvbN7 https://t.co/xJoStGBSqp",Efficient Representation Learning via Adaptive Context Pooling,163
+22,1544488616734429185,"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
+23,1544485593991811072,"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
+24,1544483235542990856,"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
+25,1544207617102331906,"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
+26,1544201186739458049,"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
+27,1544193877053161480,"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
+28,1544127293660037120,"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
+29,1543981460964708352,"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
+30,1543766808309669889,"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
+31,1543763117515182082,"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
+32,1543759817449390080,"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
+33,1543757524356272134,"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
+34,1542740430084792320,"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
+35,1542713456268304384,"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
+36,1542712192746782720,"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
+37,1542709853516431361,"Learning Iterative Reasoning through Energy Minimization
+abs: https://t.co/WDLx1hKPqG
+project page:… https://t.co/oDEClr0ho1",Learning Iterative Reasoning through Energy Minimization,125
+38,1542709029964849154,"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
+39,1542325850036752394,"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
+40,1542316111743664133,"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
+41,1542313347835731970,"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
+42,1542312585768435712,"3D-Aware Video Generation
+abs: https://t.co/N64ARXFKMJ
+project page: https://t.co/5MoGVKqItn https://t.co/uZdLIXWc1P",3D-Aware Video Generation,122
+43,1541957148070010881,"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
+44,1541948699559006210,"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
+45,1541945827035332610,"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
+46,1541626617490837504,"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
+47,1541599748624351233,"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
+48,1541592312094101506,"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
+49,1541590513241006080,"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
+50,1541588372631818241,"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
+51,1541226747533922308,"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
+52,1541219433259175937,"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
+53,1540184734390706176,"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
+54,1540176838017916933,"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
+55,1540161095930880001,"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
+56,1540156319923060736,"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
+57,1539811680359796739,"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
+58,1539794210190155778,"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
+59,1539780412297330689,"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
+60,1539777865688010753,"reStructured Pre-training
+abs: https://t.co/mYm7qbt59N https://t.co/O5T3tSY4PL",reStructured Pre-training,32
+61,1539672920456298498,"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
+62,1539480179151712256,"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
+63,1539460213211910150,"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
+64,1539459120667021312,"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
+65,1539453554578055168,"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
+66,1539435374103220226,"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
+67,1539421251076247554,"(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
+68,1539076449788997632,"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
+69,1538710356444471296,"Fast Finite Width Neural Tangent Kernel
+abs: https://t.co/iY1lFoYMjA https://t.co/hWzzcCd5OZ",Fast Finite Width Neural Tangent Kernel,23
+70,1538706936211951617,"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
+71,1538698653493338114,"Bootstrapped Transformer for Offline Reinforcement Learning
+abs: https://t.co/YiEY3uiTgL https://t.co/yle4hPgMmf",Bootstrapped Transformer for Offline Reinforcement Learning,137
+72,1538695457550921728,"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
+73,1538692524830769152,"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
+74,1538687423722541056,"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
+75,1538686489491648514,"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
+76,1538006265363738625,"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
+77,1538000649933115393,"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
+78,1537924151389736961,"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
+79,1537640654968324099,"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
+80,1537637590274277376,"MoDi: Unconditional Motion Synthesis from Diverse Data
+abs: https://t.co/YBV9jSUemo https://t.co/o1uvG18RSk",MoDi: Unconditional Motion Synthesis from Diverse Data,70
+81,1537630146244517889,"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
+82,1537622879386456064,"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
+83,1537621348339572736,"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
+84,1537618457365303296,"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
+85,1537323042380124160,"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
+86,1537314480056672258,"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
+87,1537288570880368640,"Masked Siamese ConvNets
+abs: https://t.co/YMG1O1ZZ5N https://t.co/LCVqVvFNfR",Masked Siamese ConvNets,83
+88,1537265816609116161,"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
+89,1537257011657814016,"Variable Bitrate Neural Fields
+abs: https://t.co/Rp1t2LaQaW
+project page: https://t.co/e2t8OrznxI https://t.co/6hw7OwbjZN",Variable Bitrate Neural Fields,162
+90,1537254679188488195,"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
+91,1537252952666087424,"Prefix Language Models are Unified Modal Learners
+abs: https://t.co/BD4b3rQnKg https://t.co/2ofScnMIKN",Prefix Language Models are Unified Modal Learners,66
+92,1537248480074293251,"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
+93,1536879515883945984,"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
+94,1536872875885580288,"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
+95,1536871347372052480,"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
+96,1536526888289574915,"Large-Scale Retrieval for Reinforcement Learning
+abs: https://t.co/fjzGvI3ZXB https://t.co/eFRHt8yXoq",Large-Scale Retrieval for Reinforcement Learning,86
+97,1536522198785183744,"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
+98,1536521362898145280,"Self-critiquing models for assisting human evaluators
+abs: https://t.co/8Zy2xfA5Qz https://t.co/qndZMS9zXa",Self-critiquing models for assisting human evaluators,19
+99,1536515535202136064,"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
+100,1536493418305703938,"How Much is Enough? A Study on Diffusion Times in Score-based Generative Models
+abs: https://t.co/qFEZBDrdrq https://t.co/iBlNs4iNE2",How Much is Enough? A Study on Diffusion Times in Score-based Generative Models,60
+101,1536491133513129990,"Meta Optimal Transport
+abs: https://t.co/UKdYXKA8Vd
+github: https://t.co/xb9FVcim7g
+
+Meta OT models surpass the sta… https://t.co/OlfwZIC52r",Meta Optimal Transport,67
+102,1535656084488192005,"Neural Prompt Search
+abs: https://t.co/wZTUHIcqdv
+github: https://t.co/vnYEMBrKzt
+
+view existing parameter-efficien… https://t.co/pLvxNt84gV",Neural Prompt Search,174
+103,1535521674233319424,"Deep Surrogate Assisted Generation of Environments
+abs: https://t.co/1RYhxJ71tt
+project page:… https://t.co/5MuAOKIePA",Deep Surrogate Assisted Generation of Environments,58
+104,1535521046257975297,"Deep Hierarchical Planning from Pixels
+abs: https://t.co/xXBDevsRnK
+project page: https://t.co/LoNsGVecaR https://t.co/K7RKIq2hBT",Deep Hierarchical Planning from Pixels,101
+105,1535506620624642048,"VN-Transformer: Rotation-Equivariant Attention for Vector Neurons
+abs: https://t.co/OkS58YpYq8 https://t.co/ailLjhzsqa",VN-Transformer: Rotation-Equivariant Attention for Vector Neurons,144
+106,1535469100436271105,"Factuality Enhanced Language Models for Open-Ended Text Generation
+abs: https://t.co/YX83NnfpMU
+
+factual-nucleus sa… https://t.co/suFGgO8Ajv",Factuality Enhanced Language Models for Open-Ended Text Generation,31
+107,1535449832332177408,"Unveiling Transformers with LEGO: a synthetic reasoning task
+abs: https://t.co/FCnAD9AjMY https://t.co/LsUblvE3Ig",Unveiling Transformers with LEGO: a synthetic reasoning task,77
+108,1535392356068892674,"BigVGAN: A Universal Neural Vocoder with Large-Scale Training
+abs: https://t.co/4NRS1WBePa
+project page:… https://t.co/rpuKyOEGMH",BigVGAN: A Universal Neural Vocoder with Large-Scale Training,170
+109,1535069067052195862,"Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
+abs:… https://t.co/v2aIh9B5H2",Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models,158
+110,1535067850435600403,"Draft-and-Revise: Effective Image Generation with Contextual RQ-Transformer
+abs: https://t.co/0s94Tbwh3q
+
+propose i… https://t.co/lQZWEHXeRI",Draft-and-Revise: Effective Image Generation with Contextual RQ-Transformer,52
+111,1535066703075352601,"VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolution
+abs:… https://t.co/UKXo53aomf",VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolution,146
+112,1535061799975919633,"Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem
+abs:… https://t.co/fUyM4hz22a",Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem,48
+113,1535026713100537871,"Sparse Fusion Mixture-of-Experts are Domain Generalizable Learners
+abs: https://t.co/koYO5SuiDQ
+github:… https://t.co/1xMmVzboCC",Sparse Fusion Mixture-of-Experts are Domain Generalizable Learners,70
+114,1534712305790894081,"STable: Table Generation Framework for Encoder-Decoder Models
+abs: https://t.co/P8GcsztVFp https://t.co/lJnhODKXyn",STable: Table Generation Framework for Encoder-Decoder Models,32
+115,1534702470202630144,"Neural Diffusion Processes
+abs: https://t.co/do2pFgpRWY
+
+empirically show that NDPs are able to capture functional… https://t.co/Fx5BFrA9qQ",Neural Diffusion Processes,229
+116,1534701793183252485,"Patch-based Object-centric Transformers for Efficient Video Generation
+abs: https://t.co/oeAa0hiBqZ
+project page:… https://t.co/qCoaulnDfS",Patch-based Object-centric Transformers for Efficient Video Generation,30
+117,1534700653628764160,"Accelerating Score-based Generative Models for High-Resolution Image Synthesis
+abs: https://t.co/rC90ydANVJ
+project… https://t.co/5reyDDPyBN",Accelerating Score-based Generative Models for High-Resolution Image Synthesis,69
+118,1534476660355043329,"On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning
+abs: https://t.co/1gEuTB7Sf1
+
+multi-task pre… https://t.co/zx8QDoxq2l",On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning,39
+119,1534465882512146432,"Few-Shot Learning by Dimensionality Reduction in Gradient Space
+abs: https://t.co/IMwlsW0r5V
+
+introduce SubGD, a no… https://t.co/YltxH8mUtF",Few-Shot Learning by Dimensionality Reduction in Gradient Space,204
+120,1534376291453083648,"DETR++: Taming Your Multi-Scale Detection Transformer
+abs: https://t.co/kOQ5V4vC3C
+
+DETR++, a new architecture that… https://t.co/i7qtSX9eA3",DETR++: Taming Your Multi-Scale Detection Transformer,85
+121,1534347375128547328,"Intra-agent speech permits zero-shot task acquisition
+abs: https://t.co/2yVGA91kSA
+
+with ~ 150 additional image cap… https://t.co/DtBczvw7lh",Intra-agent speech permits zero-shot task acquisition,60
+122,1534343347334176770,"Universal Speech Enhancement with Score-based Diffusion
+abs: https://t.co/jv1rQ14Do4
+project page:… https://t.co/UMEE3irGWN",Universal Speech Enhancement with Score-based Diffusion,125
+123,1534341405920870400,"Generating Long Videos of Dynamic Scenes
+abs: https://t.co/SjMCJub1RO
+project page: https://t.co/c97Jcf3lcC
+
+presen… https://t.co/jgcfMwGMo6",Generating Long Videos of Dynamic Scenes,336
+124,1533997063951765506,"Zero-Shot Voice Conditioning for Denoising Diffusion TTS Models
+abs: https://t.co/iTfFppABzr
+
+method requires a sho… https://t.co/GALvAsiQ0J",Zero-Shot Voice Conditioning for Denoising Diffusion TTS Models,89
+125,1533996337557020672,"Drawing out of Distribution with Neuro-Symbolic Generative Models
+abs: https://t.co/PcRRRLIVyV
+
+DooD trained on MNI… https://t.co/h28KgM3m3k",Drawing out of Distribution with Neuro-Symbolic Generative Models,39
+126,1533993050627776512,"Separable Self-attention for Mobile Vision Transformers
+abs: https://t.co/Xj1aZMucFe
+
+With ~ 3M parameters, MobileV… https://t.co/LTag2ck7Ew",Separable Self-attention for Mobile Vision Transformers,89
+127,1533989659017199617,"Extreme Compression for Pre-trained Transformers Made Simple and Efficient
+abs: https://t.co/7epbwDmV31 https://t.co/n9nppcTgGJ",Extreme Compression for Pre-trained Transformers Made Simple and Efficient,84
+128,1533988146102288386,"On the duality between contrastive and non-contrastive self-supervised learning
+abs: https://t.co/O2GdHjqiTz https://t.co/nUibodNE9M",On the duality between contrastive and non-contrastive self-supervised learning,83
+129,1533982101653098503,"ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers
+abs:… https://t.co/tQuBWS3uaH",ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers,25
+130,1533980842867015681,"Torsional Diffusion for Molecular Conformer Generation
+abs: https://t.co/VfhEdlJLd7
+github: https://t.co/DYpXh7NbKe https://t.co/khz3yO5FFZ",Torsional Diffusion for Molecular Conformer Generation,24
+131,1533980437114232832,"Blended Latent Diffusion
+abs: https://t.co/5K8QQnlQfz
+project page: https://t.co/ztlJtR4Sio
+
+present an accelerated… https://t.co/qzrdUJc4i9",Blended Latent Diffusion,55
+132,1533979552761913344,"Diffusion-GAN: Training GANs with Diffusion
+abs: https://t.co/rxRpORfP5U
+
+DiffusionGAN can provide stable and data-… https://t.co/ScQTvm3XaA",Diffusion-GAN: Training GANs with Diffusion,237
+133,1533676404063232000,"Beyond Tabula Rasa: Reincarnating Reinforcement Learning
+abs: https://t.co/r8TcfqPyIs https://t.co/qSO5K11vYB",Beyond Tabula Rasa: Reincarnating Reinforcement Learning,34
+134,1533649732345778177,"Improving Fairness in Large-Scale Object Recognition by CrowdSourced Demographic Information
+abs:… https://t.co/3mGwmSsO6M",Improving Fairness in Large-Scale Object Recognition by CrowdSourced Demographic Information,17
+135,1533634419986153472,"Positive Unlabeled Contrastive Learning
+abs: https://t.co/LC33ii48Q6 https://t.co/eWLoasRamS",Positive Unlabeled Contrastive Learning,67
+136,1533633258545610754,"Reinforcement Learning with Neural Radiance Fields
+abs: https://t.co/8ESw75I2N9
+project page:… https://t.co/DQrpZ5dyrb",Reinforcement Learning with Neural Radiance Fields,131
+137,1533619945996697600,"Compositional Visual Generation with Composable Diffusion Models
+abs: https://t.co/FEKYaDOlwf
+project page:… https://t.co/qvaTyuj3un",Compositional Visual Generation with Composable Diffusion Models,122
+138,1533611409069711368,"Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules
+abs:… https://t.co/rQTNT4yfcB",Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules,40
+139,1532729442321170433,"Deep Learning on Implicit Neural Datasets
+abs: https://t.co/nPGleDBRSq
+
+introduce the INR-Net, the first general fr… https://t.co/i1xT7bLhSN",Deep Learning on Implicit Neural Datasets,81
+140,1532726423697465344,"SupMAE: Supervised Masked Autoencoders Are Efficient Vision Learners
+abs: https://t.co/SIR2ufE89J
+github:… https://t.co/tZoNFvtDFQ",SupMAE: Supervised Masked Autoencoders Are Efficient Vision Learners,178
+141,1532558380119752705,"DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks
+abs:… https://t.co/dHBUdpmqm9",DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks,31
+142,1532554016072376323,"Cascaded Video Generation for Videos In-the-Wild
+abs: https://t.co/wDkiRCEWXN https://t.co/GJSVK80qC0",Cascaded Video Generation for Videos In-the-Wild,57
+143,1532547568567300096,"Finding the Right Recipe for Low Resource Domain Adaptation in Neural Machine Translation
+abs:… https://t.co/FAEEhSyQpY",Finding the Right Recipe for Low Resource Domain Adaptation in Neural Machine Translation,12
+144,1532540853071265799,"BayesFormer: Transformer with Uncertainty Estimation
+abs: https://t.co/0OqGgau2D2
+
+introduce BayesFormer, a Transfo… https://t.co/znYfXmUPpJ",BayesFormer: Transformer with Uncertainty Estimation,188
+145,1532539121662574605,"Improving Diffusion Models for Inverse Problems using Manifold Constraints
+abs: https://t.co/Mt78QlNgZZ https://t.co/d6T7XFkqf1",Improving Diffusion Models for Inverse Problems using Manifold Constraints,115
+146,1532538212438130697,"DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps
+abs:… https://t.co/PBn2cEeEle",DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps,93
+147,1532201565167267840,"Hopular: Modern Hopfield Networks for Tabular Data
+abs: https://t.co/O5h6GYoGZd
+github: https://t.co/kztLUsmzMY
+pro… https://t.co/xqlUFoil7K",Hopular: Modern Hopfield Networks for Tabular Data,485
+148,1532173830428442627,"PandA: Unsupervised Learning of Parts and Appearances in the Feature Maps of GANs
+abs: https://t.co/MdoshW31xe
+gith… https://t.co/d0PWKpIufP",PandA: Unsupervised Learning of Parts and Appearances in the Feature Maps of GANs,121
+149,1532162242715721728,"Elucidating the Design Space of Diffusion-Based Generative Models
+abs: https://t.co/WtodJSq1wa
+
+improve efficiency… https://t.co/Fp84kzysBZ",Elucidating the Design Space of Diffusion-Based Generative Models,257
+150,1531810146178957312,"Chefs' Random Tables: Non-Trigonometric Random Features
+abs: https://t.co/qrt5BnhG2g https://t.co/AuWq9HKnl5",Chefs' Random Tables: Non-Trigonometric Random Features,19
+151,1531802121280147457,"Few-Shot Diffusion Models
+abs: https://t.co/Oz75eOx0Ue
+
+At test time, the model is able to generate samples from pr… https://t.co/qw3Wdivfks",Few-Shot Diffusion Models,114
+152,1531798720550952961,"SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image collections
+abs: https://t.co/eviBoaJ1Zw… https://t.co/XsdD2CSafR",SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image collections,148
+153,1531484127177936896,"Play it by Ear: Learning Skills amidst Occlusion through Audio-Visual Imitation Learning
+abs:… https://t.co/yafGze7shH",Play it by Ear: Learning Skills amidst Occlusion through Audio-Visual Imitation Learning,36
+154,1531466054492364800,"Dataset Condensation via Efficient Synthetic-Data Parameterization
+abs: https://t.co/IA66WHQQCH
+github:… https://t.co/PuBEVyx5EK",Dataset Condensation via Efficient Synthetic-Data Parameterization,110
+155,1531465172262567937,"Neural Shape Mating: Self-Supervised Object Assembly with Adversarial Shape Priors
+abs: https://t.co/25EYR1yE1A
+pro… https://t.co/qdqxXZtyYx",Neural Shape Mating: Self-Supervised Object Assembly with Adversarial Shape Priors,56
+156,1531460153152786432,"Teaching Models to Express Their Uncertainty in Words
+abs: https://t.co/rKcZNhBLt5
+
+GPT-3 model can learn to expres… https://t.co/Z3YCzXqaMX",Teaching Models to Express Their Uncertainty in Words,163
+157,1531454478968406016,"Temporal Latent Bottleneck: Synthesis of Fast and Slow Processing Mechanisms in Sequence Learning
+abs:… https://t.co/U47eMKEmf3",Temporal Latent Bottleneck: Synthesis of Fast and Slow Processing Mechanisms in Sequence Learning,36
+158,1531451492120535041,"Gating Dropout: Communication-efficient Regularization for Sparsely Activated Transformers
+abs:… https://t.co/Ar0fNxMRi9",Gating Dropout: Communication-efficient Regularization for Sparsely Activated Transformers,28
+159,1531445364217237509,"Prompting ELECTRA: Few-Shot Learning with Discriminative Pre-Trained Models
+abs: https://t.co/myWID3paI2 https://t.co/S0WUP71wz8",Prompting ELECTRA: Few-Shot Learning with Discriminative Pre-Trained Models,66
+160,1531444059780308996,"Neural Volumetric Object Selection
+abs: https://t.co/ZLiJ5iBZzQ
+project page: https://t.co/YGsNO14XK7 https://t.co/4twrRcyExx",Neural Volumetric Object Selection,97
+161,1531442002814025728,"Multi-Game Decision Transformers
+abs: https://t.co/5JtgTx3B49
+project page: https://t.co/rKk7h7wLga
+
+a single trans… https://t.co/zcJXA5tDhR",Multi-Game Decision Transformers,105
+162,1531440090161025024,"Diffusion-LM Improves Controllable Text Generation
+abs: https://t.co/YYVX2fuWrM
+
+Diffusion-LM iteratively denoises… https://t.co/1pJ5djHV9T",Diffusion-LM Improves Controllable Text Generation,145
+163,1531176037400338432,"MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control
+abs: https://t.co/HpRvGT2UDz
+project… https://t.co/6noxiVtz85",MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control,47
+164,1531174102572191744,"Neural Basis Models for Interpretability
+abs: https://t.co/u0G7oK87X4 https://t.co/ML7UCNPDkP",Neural Basis Models for Interpretability,55
+165,1531173694214656005,"Scalable Interpretability via Polynomials
+abs: https://t.co/EKZDra09oM https://t.co/XyIoQHWftG",Scalable Interpretability via Polynomials,32
+166,1531173081393336320,"Sharpness-Aware Training for Free
+abs: https://t.co/R6SSrWAjL2 https://t.co/alHDGt3zQo",Sharpness-Aware Training for Free,155
+167,1531165352037691392,"Global Normalization for Streaming Speech Recognition in a Modular Framework
+abs: https://t.co/OfIb7wiVkx
+
+demonstr… https://t.co/0iVBVXVBBs",Global Normalization for Streaming Speech Recognition in a Modular Framework,21
+168,1531104909927628806,"Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions
+abs: https://t.co/gVXiOx5Df3 https://t.co/eufEJbHHRr",Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions,47
+169,1531100741166833664,"FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
+abs: https://t.co/3aHeecihur
+
+an IO-awa… https://t.co/GoJsOKYEgt",FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness,233
+170,1531098962932944896,"Contrastive Siamese Network for Semi-supervised Speech Recognition
+abs: https://t.co/SL374ByjZO
+
+experiments show t… https://t.co/efVonWBQC5",Contrastive Siamese Network for Semi-supervised Speech Recognition,71
+171,1531096569365282816,"X-ViT: High Performance Linear Vision Transformer without Softmax
+abs: https://t.co/A6HZ2vXKDB https://t.co/kArY0Tm4VE",X-ViT: High Performance Linear Vision Transformer without Softmax,120
+172,1531093245308059650,"Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval
+
+transformer… https://t.co/OSLGlyUNqb",Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval,12
+173,1531092289090736129,"Quark: Controllable Text Generation with Reinforced Unlearning
+abs: https://t.co/OmS9AqhC7d
+
+introduce Quantized Re… https://t.co/M4DHSUpwF3",Quark: Controllable Text Generation with Reinforced Unlearning,144
+174,1531091654567919616,"Training and Inference on Any-Order Autoregressive Models the Right Way
+abs: https://t.co/G8DNeKtoJK
+
+leads to impr… https://t.co/JjXafy7iAu",Training and Inference on Any-Order Autoregressive Models the Right Way,22
+175,1531090584231890947,"Contrastive Learning Rivals Masked Image Modeling in Fine-tuning via Feature Distillation
+abs:… https://t.co/binMlc2scV",Contrastive Learning Rivals Masked Image Modeling in Fine-tuning via Feature Distillation,52
+176,1531089687263293442,"Maximum Likelihood Training of Implicit Nonlinear Diffusion Models
+abs: https://t.co/U2YtYUURqH https://t.co/lw7hcspT7o",Maximum Likelihood Training of Implicit Nonlinear Diffusion Models,110
+177,1531088458839740416,"Why So Pessimistic? Estimating Uncertainties for Offline RL through Ensembles, and Why Their Independence Matters
+a… https://t.co/e1H5ZyvcQg","Why So Pessimistic? Estimating Uncertainties for Offline RL through Ensembles, and Why Their Independence Matters",20
+178,1531086920461307906,"Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean Measures
+abs:… https://t.co/7DWwix1kP1","Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean Measures",81
+179,1531017163284393987,"CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers
+github: https://t.co/1JuOHU7puc https://t.co/Wilcq2Xxb9",CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers,1498
+180,1530278551676657665,"Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality
+abs: https://t.co/swtjYLryr5 https://t.co/Ny4wTtkaAI",Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality,31
+181,1530029153101168645,"Towards Learning Universal Hyperparameter Optimizers with Transformers
+abs: https://t.co/yON7zKZCRy
+
+extensive expe… https://t.co/UWv7nrCmhF",Towards Learning Universal Hyperparameter Optimizers with Transformers,129
+182,1530028097692647449,"BiT: Robustly Binarized Multi-distilled Transformer
+abs: https://t.co/buQ40Vo9ee https://t.co/Q8iyC2Auql",BiT: Robustly Binarized Multi-distilled Transformer,37
+183,1530018008667660300,"Evaluating Multimodal Interactive Agents
+abs: https://t.co/CtrOihrZBZ https://t.co/sThFVydSUZ",Evaluating Multimodal Interactive Agents,23
+184,1530013711645253632,"Matryoshka Representations for Adaptive Deployment
+abs: https://t.co/KkqN7sxmnN
+
+flexibility within the learned Mat… https://t.co/RYra48uEKN",Matryoshka Representations for Adaptive Deployment,69
+185,1530010193836244992,"Green Hierarchical Vision Transformer for Masked Image Modeling
+abs: https://t.co/r4Y9LfE4HC
+github:… https://t.co/o7ZihujhkM",Green Hierarchical Vision Transformer for Masked Image Modeling,26
+186,1529673576835698691,"Inception Transformer
+abs: https://t.co/EoPDBOafSS
+
+iFormer-S hits the top-1 accuracy of 83.4% on ImageNet-1K, much… https://t.co/24J3SnTBdm",Inception Transformer,117
+187,1529640184081534977,"FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech
+abs: https://t.co/IABvUreqHv https://t.co/iUUzNPaPFp",FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech,30
+188,1529637573462831105,"Autoformalization with Large Language Models
+abs: https://t.co/SoGYXkMGhV
+
+methodology results in a new state-of-th… https://t.co/pTxpC00QFC",Autoformalization with Large Language Models,24
+189,1529630110885851136,"AdaMix: Mixture-of-Adapter for Parameter-efficient Tuning of Large Language Models
+abs: https://t.co/aD0daO7HEa
+
+By… https://t.co/NW3DbOJdwH",AdaMix: Mixture-of-Adapter for Parameter-efficient Tuning of Large Language Models,64
+190,1529625016471633920,"An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems
+abs:… https://t.co/gks4xeDd22",An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems,10
+191,1529341790335246336,"Policy Compliance Detection via Expression Tree Inference
+abs: https://t.co/Ic7Wm852Qz https://t.co/4RtEnug1RD",Policy Compliance Detection via Expression Tree Inference,8
+192,1529309686318653441,"History Compression via Language Models in Reinforcement Learning
+abs: https://t.co/N1smkJUAW9 https://t.co/4v1an4CkTU",History Compression via Language Models in Reinforcement Learning,85
+193,1529303237572034560,"On the Role of Bidirectionality in Language Model Pre-Training
+abs: https://t.co/fG2SbUhB1W
+
+propose a new framewor… https://t.co/Gc40i0zyeV",On the Role of Bidirectionality in Language Model Pre-Training,26
+194,1529301315221917699,"Large Language Models are Zero-Shot Reasoners
+abs: https://t.co/GgdLms77wF
+
+LLMs are decent zero-shot reasoners by… https://t.co/PTH6QpdSo2",Large Language Models are Zero-Shot Reasoners,85
+195,1529278657856000000,"Naive Few-Shot Learning: Sequence Consistency Evaluation
+abs: https://t.co/ySAzuujz2O https://t.co/aVVLHJdBUC",Naive Few-Shot Learning: Sequence Consistency Evaluation,19
+196,1529075001256824834,"All Birds with One Stone: Multi-task Text Classification for Efficient Inference with One Forward Pass
+abs:… https://t.co/fcPGWaFEk5",All Birds with One Stone: Multi-task Text Classification for Efficient Inference with One Forward Pass,12
+197,1529071850860453888,"StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models
+abs:… https://t.co/MDT1Bxw9by",StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models,20
+198,1528909940324192256,"Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methods
+abs:… https://t.co/B65LGrnCLg",Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methods,38
+199,1528907841335066625,"Flexible Diffusion Modeling of Long Videos
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+demonstrate improved video modeling over p… https://t.co/Y15RoaMAFg",Flexible Diffusion Modeling of Long Videos,84
+200,1528904484553900034,"Scaling Laws and Interpretability of Learning from Repeated Data
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+performance of 800M… https://t.co/4HHdSCe8ZT",Scaling Laws and Interpretability of Learning from Repeated Data,46
+201,1528851863306752000,"Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
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+abs: https://t.co/rE7gjT0COx https://t.co/EtbaT2jTle",Self-Supervised Depth Estimation with Isometric-Self-Sample-Based Learning,116
+203,1528576691152494592,"Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors
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+github:… https://t.co/efonU0Az1m",Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors,59
+204,1528569396456828929,"Why GANs are overkill for NLP
+abs: https://t.co/zwjCFxh22z https://t.co/tuM1ufFC7x",Why GANs are overkill for NLP,139
+205,1528555058916429828,"Lossless Acceleration for Seq2seq Generation with Aggressive Decoding
+abs: https://t.co/7bmGFXe47E
+github:… https://t.co/bXfTVfP56t",Lossless Acceleration for Seq2seq Generation with Aggressive Decoding,48
+206,1528541664561844229,"Planning with Diffusion for Flexible Behavior Synthesis
+abs: https://t.co/HSoQhC6WBV
+project page:… https://t.co/PA69vLOYmb",Planning with Diffusion for Flexible Behavior Synthesis,119
+207,1527765335528591361,"Disentangling Visual Embeddings for Attributes and Objects
+abs: https://t.co/QlDsekM1rH https://t.co/YfsJGNzjlX",Disentangling Visual Embeddings for Attributes and Objects,253
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+github:… https://t.co/GfRxgf4hKe",RankGen: Improving Text Generation with Large Ranking Models,37
+209,1527450826343604233,"Robust and Efficient Medical Imaging with Self-Supervision
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+strategy leads to strong d… https://t.co/ptwGGG2NkL",Robust and Efficient Medical Imaging with Self-Supervision,45
+210,1527097137825202177,"Masked Autoencoders As Spatiotemporal Learners
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+MAE method can learn strong representa… https://t.co/KX2kb7Zf0m",Masked Autoencoders As Spatiotemporal Learners,288
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+abs: https://t.co/pDKyAXyGmg https://t.co/ExJQyGQefn",Meta-Learning Sparse Compression Networks,34
+212,1526825242026512385,"An Empirical Investigation of Representation Learning for Imitation
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+abs: https://t.co/kKjj0oKSyE https://t.co/bk1DPxlwZ9",Planning to Practice: Efficient Online Fine-Tuning by Composing Goals in Latent Space,49
+214,1526738002621472768,"SKILL: Structured Knowledge Infusion for Large Language Models
+abs: https://t.co/vbExGmg4hx https://t.co/3hVTWxLVE1",SKILL: Structured Knowledge Infusion for Large Language Models,46
+215,1526435187093123072,"FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive Summarization
+abs:… https://t.co/qG4s6MlGmd",FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive Summarization,46
+216,1526428632868167682,"PrefixRL: Optimization of Parallel Prefix Circuits using Deep Reinforcement Learning
+abs: https://t.co/JTAU1vnmst https://t.co/fyonWO1rKa",PrefixRL: Optimization of Parallel Prefix Circuits using Deep Reinforcement Learning,45
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+abs: https://t.co/VdSXsTahOY https://t.co/lFxumNcuIj",Diffusion Models for Adversarial Purification,135