{"list":"other","total":100,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"id":"2112.10752","title":"High-Resolution Image Synthesis with Latent Diffusion Models","created":"2021-12-20","cited_by_count":18863},{"id":"1910.10683","title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","created":"2019-10-23","cited_by_count":14599},{"id":"1907.11692","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","created":"2019-07-26","cited_by_count":13372},{"id":"2002.05709","title":"A Simple Framework for Contrastive Learning of Visual Representations","created":"2020-02-13","cited_by_count":12049},{"id":"1409.0575","title":"ImageNet Large Scale Visual Recognition Challenge","created":"2014-09-01","cited_by_count":8498},{"id":"2001.08361","title":"Scaling Laws for Neural Language Models","created":"2020-01-23","cited_by_count":6586},{"id":"1409.0473","title":"Neural Machine Translation by Jointly Learning to Align and Translate","created":"2014-09-01","cited_by_count":4548},{"id":"1301.3781","title":"Efficient Estimation of Word Representations in Vector Space","created":"2013-01-16","cited_by_count":4375},{"id":"1910.01108","title":"DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter","created":"2019-10-02","cited_by_count":4307},{"id":"2101.03961","title":"Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity","created":"2021-01-11","cited_by_count":3073},{"id":"2203.15556","title":"Training Compute-Optimal Large Language Models","created":"2022-03-29","cited_by_count":3019},{"id":"1906.08237","title":"XLNet: Generalized Autoregressive Pretraining for Language Understanding","created":"2019-06-19","cited_by_count":2811},{"id":"1909.11942","title":"ALBERT: A Lite BERT for Self-supervised Learning of Language Representations","created":"2019-09-26","cited_by_count":2790},{"id":"2106.08254","title":"BEiT: BERT Pre-Training of Image Transformers","created":"2021-06-15","cited_by_count":2375},{"id":"1802.05365","title":"Deep contextualized word representations","created":"2018-02-15","cited_by_count":2290},{"id":"1603.05027","title":"Identity Mappings in Deep Residual Networks","created":"2016-03-16","cited_by_count":1965},{"id":"1901.08746","title":"BioBERT: a pre-trained biomedical language representation model for biomedical text mining","created":"2019-01-25","cited_by_count":1947},{"id":"1909.08053","title":"Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism","created":"2019-09-17","cited_by_count":1868},{"id":"1901.02860","title":"Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context","created":"2019-01-09","cited_by_count":1850},{"id":"2006.05525","title":"Knowledge Distillation: A Survey","created":"2020-06-09","cited_by_count":1603},{"id":"1409.1259","title":"On the Properties of Neural Machine Translation: Encoder-Decoder Approaches","created":"2014-09-03","cited_by_count":1304},{"id":"1908.03557","title":"VisualBERT: A Simple and Performant Baseline for Vision and Language","created":"2019-08-09","cited_by_count":1296},{"id":"1912.08777","title":"PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization","created":"2019-12-18","cited_by_count":1102},{"id":"1901.07291","title":"Cross-lingual Language Model Pretraining","created":"2019-01-22","cited_by_count":1090},{"id":"1907.10529","title":"SpanBERT: Improving Pre-training by Representing and Predicting Spans","created":"2019-07-24","cited_by_count":834},{"id":"1909.05858","title":"CTRL: A Conditional Transformer Language Model for Controllable Generation","created":"2019-09-11","cited_by_count":762},{"id":"1904.01766","title":"VideoBERT: A Joint Model for Video and Language Representation Learning","created":"2019-04-03","cited_by_count":703},{"id":"2006.10503","title":"SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks","created":"2020-06-18","cited_by_count":607},{"id":"2003.03123","title":"Directional Message Passing for Molecular Graphs","created":"2020-03-06","cited_by_count":561},{"id":"1905.12616","title":"Defending Against Neural Fake News","created":"2019-05-29","cited_by_count":559},{"id":"2201.11990","title":"Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model","created":"2022-01-28","cited_by_count":559},{"id":"1603.01360","title":"Neural Architectures for Named Entity Recognition","created":"2016-03-04","cited_by_count":550},{"id":"1905.07129","title":"ERNIE: Enhanced Language Representation with Informative Entities","created":"2019-05-17","cited_by_count":528},{"id":"2009.03509","title":"Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification","created":"2020-09-08","cited_by_count":506},{"id":"1908.06066","title":"Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-training","created":"2019-08-16","cited_by_count":451},{"id":"1908.10063","title":"FinBERT: Financial Sentiment Analysis with Pre-trained Language Models","created":"2019-08-27","cited_by_count":385},{"id":"1912.12180","title":"Axial Attention in Multidimensional Transformers","created":"2019-12-20","cited_by_count":358},{"id":"1911.06136","title":"KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation","created":"2019-11-13","cited_by_count":356},{"id":"1901.11504","title":"Multi-Task Deep Neural Networks for Natural Language Understanding","created":"2019-01-31","cited_by_count":350},{"id":"1905.02450","title":"MASS: Masked Sequence to Sequence Pre-training for Language Generation","created":"2019-05-07","cited_by_count":347},{"id":"2106.07139","title":"Pre-Trained Models: Past, Present and Future","created":"2021-06-14","cited_by_count":319},{"id":"1703.03130","title":"A Structured Self-attentive Sentence Embedding","created":"2017-03-09","cited_by_count":318},{"id":"2009.01411","title":"Learning from Protein Structure with Geometric Vector Perceptrons","created":"2020-09-03","cited_by_count":313},{"id":"1909.07606","title":"K-BERT: Enabling Language Representation with Knowledge Graph","created":"2019-09-17","cited_by_count":304},{"id":"1904.09223","title":"ERNIE: Enhanced Representation through Knowledge Integration","created":"2019-04-19","cited_by_count":302},{"id":"1909.04164","title":"Knowledge Enhanced Contextual Word Representations","created":"2019-09-09","cited_by_count":296},{"id":"2206.11990","title":"Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs","created":"2022-06-23","cited_by_count":264},{"id":"1708.02709","title":"Recent Trends in Deep Learning Based Natural Language Processing","created":"2017-08-09","cited_by_count":263},{"id":"2103.04922","title":"Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models","created":"2021-03-08","cited_by_count":252},{"id":"1906.08230","title":"Evaluating Protein Transfer Learning with TAPE","created":"2019-06-19","cited_by_count":231},{"id":"2002.12804","title":"UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training","created":"2020-02-28","cited_by_count":204},{"id":"2202.10447","title":"Transformer Quality in Linear Time","created":"2022-02-21","cited_by_count":191},{"id":"2202.03104","title":"SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation","created":"2022-02-07","cited_by_count":171},{"id":"2007.06225","title":"ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing","created":"2020-07-13","cited_by_count":162},{"id":"2203.06125","title":"Protein Representation Learning by Geometric Structure Pretraining","created":"2022-03-11","cited_by_count":162},{"id":"2206.13517","title":"ProGen2: Exploring the Boundaries of Protein Language Models","created":"2022-06-27","cited_by_count":159},{"id":"1704.01444","title":"Learning to Generate Reviews and Discovering Sentiment","created":"2017-04-05","cited_by_count":148},{"id":"2004.03497","title":"ProGen: Language Modeling for Protein Generation","created":"2020-03-08","cited_by_count":128},{"id":"2209.15611","title":"Protein structure generation via folding diffusion","created":"2022-09-30","cited_by_count":115},{"id":"2110.14883","title":"Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training","created":"2021-10-28","cited_by_count":96},{"id":"1904.02099","title":"75 Languages, 1 Model: Parsing Universal Dependencies Universally","created":"2019-04-03","cited_by_count":90},{"id":"2110.05006","title":"Pre-trained Language Models in Biomedical Domain: A Systematic Survey","created":"2021-10-11","cited_by_count":87},{"id":"2209.12643","title":"PiFold: Toward effective and efficient protein inverse folding","created":"2022-09-22","cited_by_count":83},{"id":"1904.05811","title":"Relational Graph Attention Networks","created":"2019-04-11","cited_by_count":82},{"id":"2205.13760","title":"Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval","created":"2022-05-27","cited_by_count":79},{"id":"2206.02096","title":"PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding","created":"2022-06-05","cited_by_count":77},{"id":"2201.11147","title":"OntoProtein: Protein Pretraining With Gene Ontology Embedding","created":"2022-01-23","cited_by_count":70},{"id":"1902.08661","title":"Learning protein sequence embeddings using information from structure","created":"2019-02-22","cited_by_count":51},{"id":"2205.05789","title":"RITA: a Study on Scaling Up Generative Protein Sequence Models","created":"2022-05-11","cited_by_count":42},{"id":"2202.01079","title":"AlphaDesign: A graph protein design method and benchmark on AlphaFoldDB","created":"2022-02-01","cited_by_count":40},{"id":"1312.4569","title":"Dropout improves Recurrent Neural Networks for Handwriting Recognition","created":"2013-11-05","cited_by_count":39},{"id":"1904.09482","title":"Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding","created":"2019-04-20","cited_by_count":37},{"id":"2106.07431","title":"CRASH: Raw Audio Score-based Generative Modeling for Controllable High-resolution Drum Sound Synthesis","created":"2021-06-14","cited_by_count":37},{"id":"2204.04213","title":"Structure-aware Protein Self-supervised Learning","created":"2022-04-06","cited_by_count":33},{"id":"1609.00680","title":"Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model","created":"2016-09-02","cited_by_count":31},{"id":"2002.07972","title":"The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding","created":"2020-02-19","cited_by_count":28},{"id":"1609.07959","title":"Multiplicative LSTM for sequence modelling","created":"2016-09-26","cited_by_count":27},{"id":"2002.12620","title":"TextBrewer: An Open-Source Knowledge Distillation Toolkit for Natural Language Processing","created":"2020-02-28","cited_by_count":26},{"id":"2106.13058","title":"Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design","created":"2021-06-24","cited_by_count":23},{"id":"2011.04640","title":"Scaling Hidden Markov Language Models","created":"2020-11-09","cited_by_count":22},{"id":"2004.13167","title":"Energy-based models for atomic-resolution protein conformations","created":"2020-04-27","cited_by_count":20},{"id":"2110.15527","title":"Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model","created":"2021-10-29","cited_by_count":20},{"id":"2207.01586","title":"Accurate RNA 3D structure prediction using a language model-based deep learning approach","created":"2022-07-04","cited_by_count":19},{"id":"1909.06814","title":"Automatically Extracting Challenge Sets for Non local Phenomena in Neural Machine Translation","created":"2019-09-15","cited_by_count":15},{"id":"2203.00854","title":"FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours","created":"2022-03-02","cited_by_count":14},{"id":"2207.10851","title":"Uncertainty-aware Multi-modal Learning via Cross-modal Random Network Prediction","created":"2022-07-22","cited_by_count":13},{"id":"1512.00843","title":"Protein secondary structure prediction using deep convolutional neural fields","created":"2015-12-02","cited_by_count":12},{"id":"2012.00195","title":"Profile Prediction: An Alignment-Based Pre-Training Task for Protein Sequence Models","created":"2020-12-01","cited_by_count":12},{"id":"2207.05477","title":"HelixFold: An Efficient Implementation of AlphaFold2 using PaddlePaddle","created":"2022-07-12","cited_by_count":12},{"id":"1403.1347","title":"Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction","created":"2014-03-06","cited_by_count":8},{"id":"2102.00466","title":"Adversarial Contrastive Pre-training for Protein Sequences","created":"2021-01-31","cited_by_count":8},{"id":"1606.02979","title":"Generative Topic Embedding: a Continuous Representation of Documents (Extended Version with Proofs)","created":"2016-06-09","cited_by_count":4},{"id":"2204.01168","title":"Few Shot Protein Generation","created":"2022-04-03","cited_by_count":4},{"id":"1412.7828","title":"Protein Secondary Structure Prediction with Long Short Term Memory Networks","created":"2014-12-25","cited_by_count":3},{"id":"2206.12240","title":"PSP: Million-level Protein Sequence Dataset for Protein Structure Prediction","created":"2022-06-24","cited_by_count":3},{"id":"1605.03004","title":"MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-based Protein Structure Prediction","created":"2016-05-10","cited_by_count":1},{"id":"1611.01503","title":"Protein Secondary Structure Prediction Using Deep Multi-scale Convolutional Neural Networks and Next-Step Conditioning","created":"2016-11-04","cited_by_count":1}],"next_cursor":null}