{"list":"other","total":97,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":59932},{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":51809},{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40204},{"id":"1710.10903","title":"Graph Attention Networks","created":"2017-10-30","cited_by_count":8854},{"id":"2111.06377","title":"Masked Autoencoders Are Scalable Vision Learners","created":"2021-11-11","cited_by_count":7339},{"id":"1502.03167","title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift","created":"2015-02-11","cited_by_count":6979},{"id":"1706.02216","title":"Inductive Representation Learning on Large Graphs","created":"2017-06-07","cited_by_count":6284},{"id":"1710.09412","title":"mixup: Beyond Empirical Risk Minimization","created":"2017-10-25","cited_by_count":5155},{"id":"1506.02142","title":"Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning","created":"2015-06-06","cited_by_count":4416},{"id":"1612.01474","title":"Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles","created":"2016-12-05","cited_by_count":3888},{"id":"1905.04899","title":"CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features","created":"2019-05-13","cited_by_count":2875},{"id":"1412.3555","title":"Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling","created":"2014-12-11","cited_by_count":2341},{"id":"1510.00149","title":"Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding","created":"2015-10-01","cited_by_count":2291},{"id":"2104.08821","title":"SimCSE: Simple Contrastive Learning of Sentence Embeddings","created":"2021-04-18","cited_by_count":2168},{"id":"1708.04552","title":"Improved Regularization of Convolutional Neural Networks with Cutout","created":"2017-08-15","cited_by_count":1623},{"id":"1708.04896","title":"Random Erasing Data Augmentation","created":"2017-08-16","cited_by_count":1235},{"id":"1808.09781","title":"Self-Attentive Sequential Recommendation","created":"2018-08-20","cited_by_count":1169},{"id":"2002.02126","title":"LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation","created":"2020-02-06","cited_by_count":1134},{"id":"1912.08777","title":"PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization","created":"2019-12-18","cited_by_count":1100},{"id":"1707.07012","title":"Learning Transferable Architectures for Scalable Image Recognition","created":"2017-07-21","cited_by_count":1097},{"id":"1612.03975","title":"ConceptNet 5.5: An Open Multilingual Graph of General Knowledge","created":"2016-12-12","cited_by_count":1065},{"id":"1603.09382","title":"Deep Networks with Stochastic Depth","created":"2016-03-30","cited_by_count":973},{"id":"1506.04757","title":"Image-based Recommendations on Styles and Substitutes","created":"2015-06-15","cited_by_count":702},{"id":"1907.10903","title":"DropEdge: Towards Deep Graph Convolutional Networks on Node Classification","created":"2019-07-25","cited_by_count":679},{"id":"1905.08108","title":"Neural Graph Collaborative Filtering","created":"2019-05-20","cited_by_count":660},{"id":"1801.10247","title":"FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling","created":"2018-01-30","cited_by_count":583},{"id":"1602.07868","title":"Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks","created":"2016-02-25","cited_by_count":541},{"id":"1602.01585","title":"Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering","created":"2016-02-04","cited_by_count":517},{"id":"1506.02557","title":"Variational Dropout and the Local Reparameterization Trick","created":"2015-06-08","cited_by_count":489},{"id":"2008.07873","title":"S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization","created":"2020-08-18","cited_by_count":311},{"id":"1904.09223","title":"ERNIE: Enhanced Representation through Knowledge Integration","created":"2019-04-19","cited_by_count":301},{"id":"1701.05369","title":"Variational Dropout Sparsifies Deep Neural Networks","created":"2017-01-19","cited_by_count":265},{"id":"1810.12890","title":"DropBlock: A regularization method for convolutional networks","created":"2018-10-30","cited_by_count":261},{"id":"1705.07832","title":"Concrete Dropout","created":"2017-05-22","cited_by_count":227},{"id":"1411.4280","title":"Efficient Object Localization Using Convolutional Networks","created":"2014-11-16","cited_by_count":226},{"id":"1605.07648","title":"FractalNet: Ultra-Deep Neural Networks without Residuals","created":"2016-05-24","cited_by_count":216},{"id":"2005.11079","title":"Graph Random Neural Network for Semi-Supervised Learning on Graphs","created":"2020-05-22","cited_by_count":213},{"id":"1704.04232","title":"Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-supervised Object and Action Localization","created":"2017-04-13","cited_by_count":207},{"id":"1708.02182","title":"Regularizing and Optimizing LSTM Language Models","created":"2017-08-07","cited_by_count":205},{"id":"2106.14448","title":"R-Drop: Regularized Dropout for Neural Networks","created":"2021-06-28","cited_by_count":202},{"id":"1409.2329","title":"Recurrent Neural Network Regularization","created":"2014-09-08","cited_by_count":189},{"id":"1302.4389","title":"Maxout Networks","created":"2013-02-18","cited_by_count":186},{"id":"1708.05027","title":"Neural Factorization Machines for Sparse Predictive Analytics","created":"2017-08-16","cited_by_count":183},{"id":"1809.05343","title":"Adaptive Sampling Towards Fast Graph Representation Learning","created":"2018-09-14","cited_by_count":180},{"id":"1911.07323","title":"Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks","created":"2019-11-17","cited_by_count":177},{"id":"1512.05287","title":"A Theoretically Grounded Application of Dropout in Recurrent Neural Networks","created":"2015-12-16","cited_by_count":161},{"id":"1506.02158","title":"Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference","created":"2015-06-06","cited_by_count":159},{"id":"2109.05687","title":"Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning","created":"2021-09-13","cited_by_count":145},{"id":"2001.04086","title":"GridMask Data Augmentation","created":"2020-01-13","cited_by_count":123},{"id":"1706.05350","title":"L2 Regularization versus Batch and Weight Normalization","created":"2017-06-16","cited_by_count":121},{"id":"1904.12575","title":"Knowledge Graph Convolutional Networks for Recommender Systems","created":"2019-03-18","cited_by_count":121},{"id":"1707.05589","title":"On the State of the Art of Evaluation in Neural Language Models","created":"2017-07-18","cited_by_count":103},{"id":"1307.1493","title":"Dropout Training as Adaptive Regularization","created":"2013-07-04","cited_by_count":89},{"id":"1705.07485","title":"Shake-Shake regularization","created":"2017-05-21","cited_by_count":87},{"id":"1701.04783","title":"Joint Deep Modeling of Users and Items Using Reviews for Recommendation","created":"2017-01-17","cited_by_count":84},{"id":"1606.02891","title":"Edinburgh Neural Machine Translation Systems for WMT 16","created":"2016-06-09","cited_by_count":76},{"id":"1606.01305","title":"Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations","created":"2016-06-03","cited_by_count":54},{"id":"1703.06229","title":"Curriculum Dropout","created":"2017-03-18","cited_by_count":50},{"id":"1709.01907","title":"Deep and Confident Prediction for Time Series at Uber","created":"2017-09-06","cited_by_count":50},{"id":"1908.08039","title":"\"Mask and Infill\" : Applying Masked Language Model to Sentiment Transfer","created":"2019-08-21","cited_by_count":48},{"id":"2104.06608","title":"Search to aggregate neighborhood for graph neural network","created":"2021-04-14","cited_by_count":48},{"id":"2105.00522","title":"Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformer","created":"2021-05-02","cited_by_count":45},{"id":"1904.13310","title":"Survey of Dropout Methods for Deep Neural Networks","created":"2019-04-25","cited_by_count":44},{"id":"1912.00552","title":"Sparse Graph Attention Networks","created":"2019-12-02","cited_by_count":41},{"id":"1802.02375","title":"ShakeDrop Regularization for Deep Residual Learning","created":"2018-02-07","cited_by_count":40},{"id":"1905.13678","title":"Learning Sparse Networks Using Targeted Dropout","created":"2019-05-31","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":"1705.07283","title":"Structured Bayesian Pruning via Log-Normal Multiplicative Noise","created":"2017-05-20","cited_by_count":39},{"id":"2004.09733","title":"Train No Evil: Selective Masking for Task-Guided Pre-Training","created":"2020-04-21","cited_by_count":36},{"id":"2101.01761","title":"AutoDropout: Learning Dropout Patterns to Regularize Deep Networks","created":"2021-01-05","cited_by_count":35},{"id":"1805.09023","title":"Addressing the Item Cold-start Problem by Attribute-driven Active Learning","created":"2018-05-23","cited_by_count":33},{"id":"1908.06725","title":"Align, Mask and Select: A Simple Method for Incorporating Commonsense Knowledge into Language Representation Models","created":"2019-08-19","cited_by_count":28},{"id":"2004.13342","title":"Scheduled DropHead: A Regularization Method for Transformer Models","created":"2020-04-28","cited_by_count":28},{"id":"1506.08700","title":"Dropout as data augmentation","created":"2015-06-29","cited_by_count":27},{"id":"1909.12807","title":"Research Commentary on Recommendations with Side Information: A Survey and Research Directions","created":"2019-09-19","cited_by_count":23},{"id":"1603.05118","title":"Recurrent Dropout without Memory Loss","created":"2016-03-16","cited_by_count":22},{"id":"1904.03392","title":"Effective and Efficient Dropout for Deep Convolutional Neural Networks","created":"2019-04-06","cited_by_count":21},{"id":"2006.04270","title":"EDropout: Energy-Based Dropout and Pruning of Deep Neural Networks","created":"2020-06-07","cited_by_count":20},{"id":"1412.4736","title":"On the Inductive Bias of Dropout","created":"2014-12-15","cited_by_count":19},{"id":"1605.06465","title":"Swapout: Learning an ensemble of deep architectures","created":"2016-05-20","cited_by_count":19},{"id":"1912.03010","title":"Semantic Mask for Transformer based End-to-End Speech Recognition","created":"2019-12-06","cited_by_count":18},{"id":"1512.00242","title":"Towards Dropout Training for Convolutional Neural Networks","created":"2015-12-01","cited_by_count":17},{"id":"1711.00066","title":"Fraternal Dropout","created":"2017-10-31","cited_by_count":14},{"id":"1711.08591","title":"Regularization of Deep Neural Networks with Spectral Dropout","created":"2017-11-23","cited_by_count":14},{"id":"1602.02220","title":"Improved Dropout for Shallow and Deep Learning","created":"2016-02-06","cited_by_count":11},{"id":"1609.08017","title":"Dropout with Expectation-linear Regularization","created":"2016-09-26","cited_by_count":9},{"id":"1511.06951","title":"Gradual DropIn of Layers to Train Very Deep Neural Networks","created":"2015-11-22","cited_by_count":8},{"id":"1612.01490","title":"Whiteout: Gaussian Adaptive Noise Regularization in Deep Neural Networks","created":"2016-12-05","cited_by_count":8},{"id":"2010.11018","title":"Token Drop mechanism for Neural Machine Translation","created":"2020-10-21","cited_by_count":8},{"id":"2103.00719","title":"LocalDrop: A Hybrid Regularization for Deep Neural Networks","created":"2021-03-01","cited_by_count":8},{"id":"1902.08673","title":"Ising-Dropout: A Regularization Method for Training and Compression of Deep Neural Networks","created":"2019-02-07","cited_by_count":6},{"id":"2111.03310","title":"Adaptive Warden Strategy for Countering Network Covert Storage Channels","created":"2021-11-05","cited_by_count":6},{"id":"1812.10158","title":"Dropout Regularization in Hierarchical Mixture of Experts","created":"2018-12-25","cited_by_count":5},{"id":"1311.0701","title":"On Fast Dropout and its Applicability to Recurrent Networks","created":"2013-11-04","cited_by_count":4},{"id":"1301.5650","title":"Regularization and nonlinearities for neural language models: when are they needed?","created":"2013-01-23","cited_by_count":3},{"id":"2011.00944","title":"Deep Pairwise Hashing for Cold-start Recommendation","created":"2020-11-02","cited_by_count":2},{"id":"2011.01393","title":"GAIN: Graph Attention & Interaction Network for Inductive Semi-Supervised Learning over Large-scale Graphs","created":"2020-11-03","cited_by_count":2}],"next_cursor":null}