{"list":"other","total":59,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"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":"2203.02155","title":"Training language models to follow instructions with human feedback","created":"2022-03-04","cited_by_count":15971},{"id":"1409.1556","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","created":"2014-09-04","cited_by_count":15364},{"id":"2302.13971","title":"LLaMA: Open and Efficient Foundation Language Models","created":"2023-02-27","cited_by_count":14132},{"id":"2307.09288","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","created":"2023-07-18","cited_by_count":13028},{"id":"1608.06993","title":"Densely Connected Convolutional Networks","created":"2016-08-25","cited_by_count":6720},{"id":"1608.03983","title":"SGDR: Stochastic Gradient Descent with Warm Restarts","created":"2016-08-13","cited_by_count":5045},{"id":"1801.04381","title":"MobileNetV2: Inverted Residuals and Linear Bottlenecks","created":"2018-01-13","cited_by_count":4795},{"id":"1409.4842","title":"Going Deeper with Convolutions","created":"2014-09-17","cited_by_count":4241},{"id":"1912.04977","title":"Advances and Open Problems in Federated Learning","created":"2019-12-10","cited_by_count":3046},{"id":"1806.07572","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","created":"2018-06-20","cited_by_count":2621},{"id":"1904.00962","title":"Large Batch Optimization for Deep Learning: Training BERT in 76 minutes","created":"2019-04-01","cited_by_count":674},{"id":"1904.11955","title":"On Exact Computation with an Infinitely Wide Neural Net","created":"2019-04-26","cited_by_count":573},{"id":"1708.03888","title":"Large Batch Training of Convolutional Networks","created":"2017-08-13","cited_by_count":535},{"id":"1912.07145","title":"PyHessian: Neural Networks Through the Lens of the Hessian","created":"2019-12-16","cited_by_count":292},{"id":"1706.04454","title":"Empirical Analysis of the Hessian of Over-Parametrized Neural Networks","created":"2017-06-14","cited_by_count":290},{"id":"1711.04623","title":"Three Factors Influencing Minima in SGD","created":"2017-11-13","cited_by_count":273},{"id":"1912.09789","title":"A Survey on Distributed Machine Learning","created":"2019-12-20","cited_by_count":188},{"id":"1611.07476","title":"Eigenvalues of the Hessian in Deep Learning: Singularity and Beyond","created":"2016-11-22","cited_by_count":159},{"id":"2002.10542","title":"Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence","created":"2020-02-24","cited_by_count":137},{"id":"1905.09997","title":"Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates","created":"2019-05-24","cited_by_count":119},{"id":"2007.07989","title":"An Improved Analysis of Stochastic Gradient Descent with Momentum","created":"2020-07-15","cited_by_count":119},{"id":"1306.2119","title":"Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n)","created":"2013-06-10","cited_by_count":111},{"id":"1710.11029","title":"Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks","created":"2017-10-30","cited_by_count":109},{"id":"1812.05217","title":"Tight Analyses for Non-Smooth Stochastic Gradient Descent","created":"2018-12-13","cited_by_count":97},{"id":"2104.07705","title":"How to Train BERT with an Academic Budget","created":"2021-04-15","cited_by_count":80},{"id":"1904.12838","title":"The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares","created":"2019-04-29","cited_by_count":68},{"id":"1602.05419","title":"Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression","created":"2016-02-17","cited_by_count":64},{"id":"2207.02628","title":"The alignment property of SGD noise and how it helps select flat minima: A stability analysis","created":"2022-07-06","cited_by_count":64},{"id":"1905.10936","title":"Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback","created":"2019-05-27","cited_by_count":50},{"id":"1712.09677","title":"Momentum and Stochastic Momentum for Stochastic Gradient, Newton, Proximal Point and Subspace Descent Methods","created":"2017-12-27","cited_by_count":49},{"id":"1803.05591","title":"On the insufficiency of existing momentum schemes for Stochastic Optimization","created":"2018-03-15","cited_by_count":49},{"id":"2010.00091","title":"Error Compensated Distributed SGD Can Be Accelerated","created":"2020-09-30","cited_by_count":45},{"id":"1910.13962","title":"Understanding the Role of Momentum in Stochastic Gradient Methods","created":"2019-10-30","cited_by_count":44},{"id":"2104.11981","title":"DecentLaM: Decentralized Momentum SGD for Large-batch Deep Training","created":"2021-04-24","cited_by_count":40},{"id":"2110.06198","title":"Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression","created":"2021-10-12","cited_by_count":38},{"id":"2002.05466","title":"Convergence of a Stochastic Gradient Method with Momentum for Non-Smooth Non-Convex Optimization","created":"2020-02-13","cited_by_count":32},{"id":"1603.04136","title":"On the Influence of Momentum Acceleration on Online Learning","created":"2016-03-14","cited_by_count":24},{"id":"1803.01927","title":"Energy-entropy competition and the effectiveness of stochastic gradient descent in machine learning","created":"2018-03-05","cited_by_count":20},{"id":"2010.01618","title":"A Modular Analysis of Provable Acceleration via Polyak's Momentum: Training a Wide ReLU Network and a Deep Linear Network","created":"2020-10-04","cited_by_count":19},{"id":"2307.15196","title":"The Marginal Value of Momentum for Small Learning Rate SGD","created":"2023-07-27","cited_by_count":18},{"id":"1901.08022","title":"A Universally Optimal Multistage Accelerated Stochastic Gradient Method","created":"2019-01-23","cited_by_count":17},{"id":"2008.01805","title":"Analytic Characterization of the Hessian in Shallow ReLU Models: A Tale of Symmetry","created":"2020-08-04","cited_by_count":17},{"id":"1901.07445","title":"Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances","created":"2019-01-22","cited_by_count":16},{"id":"2110.14109","title":"Eigencurve: Optimal Learning Rate Schedule for SGD on Quadratic Objectives with Skewed Hessian Spectrums","created":"2021-10-27","cited_by_count":15},{"id":"1710.10737","title":"Linearly convergent stochastic heavy ball method for minimizing generalization error","created":"2017-10-30","cited_by_count":14},{"id":"1907.09547","title":"Stochastic algorithms with geometric step decay converge linearly on sharp functions","created":"2019-07-22","cited_by_count":14},{"id":"2206.07553","title":"On the fast convergence of minibatch heavy ball momentum","created":"2022-06-15","cited_by_count":13},{"id":"2102.07002","title":"On the Last Iterate Convergence of Momentum Methods","created":"2021-02-13","cited_by_count":10},{"id":"1906.01164","title":"A Generic Acceleration Framework for Stochastic Composite Optimization","created":"2019-06-03","cited_by_count":9},{"id":"2305.17665","title":"Acceleration of stochastic gradient descent with momentum by averaging: finite-sample rates and asymptotic normality","created":"2023-05-28","cited_by_count":9},{"id":"2201.11204","title":"On the Convergence of mSGD and AdaGrad for Stochastic Optimization","created":"2022-01-26","cited_by_count":7},{"id":"2211.17201","title":"ExtremeBERT: A Toolkit for Accelerating Pretraining of Customized BERT","created":"2022-11-30","cited_by_count":7},{"id":"1610.03774","title":"Parallelizing Stochastic Gradient Descent for Least Squares Regression: mini-batching, averaging, and model misspecification","created":"2016-10-12","cited_by_count":3},{"id":"2305.05482","title":"On adaptive stochastic heavy ball momentum for solving linear systems","created":"2023-05-09","cited_by_count":3},{"id":"1812.03934","title":"Stagewise Training Accelerates Convergence of Testing Error Over SGD","created":"2018-12-10","cited_by_count":2},{"id":"2106.02888","title":"Bandwidth-based Step-Sizes for Non-Convex Stochastic Optimization","created":"2021-06-05","cited_by_count":2}],"next_cursor":null}