{"list":"other","total":56,"rows":[{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"1412.6572","title":"Explaining and Harnessing Adversarial Examples","created":"2014-12-20","cited_by_count":7364},{"id":"1706.06083","title":"Towards Deep Learning Models Resistant to Adversarial Attacks","created":"2017-06-19","cited_by_count":6415},{"id":"1312.6114","title":"Auto-Encoding Variational Bayes","created":"2013-12-20","cited_by_count":4053},{"id":"1612.01474","title":"Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles","created":"2016-12-05","cited_by_count":3926},{"id":"1605.07146","title":"Wide Residual Networks","created":"2016-05-23","cited_by_count":3070},{"id":"1608.04644","title":"Towards Evaluating the Robustness of Neural Networks","created":"2016-08-16","cited_by_count":2998},{"id":"2106.01946","title":"Convex optimization","created":"2021-06-03","cited_by_count":2515},{"id":"2012.07436","title":"Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting","created":"2020-12-14","cited_by_count":2169},{"id":"2003.01690","title":"Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks","created":"2020-03-03","cited_by_count":1451},{"id":"1802.00420","title":"Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples","created":"2018-02-01","cited_by_count":1268},{"id":"1611.00712","title":"The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables","created":"2016-11-02","cited_by_count":1148},{"id":"1705.07204","title":"Ensemble Adversarial Training: Attacks and Defenses","created":"2017-05-19","cited_by_count":840},{"id":"1711.00165","title":"Deep Neural Networks as Gaussian Processes","created":"2017-11-01","cited_by_count":622},{"id":"2002.08791","title":"Bayesian Deep Learning and a Probabilistic Perspective of Generalization","created":"2020-02-20","cited_by_count":480},{"id":"1711.01991","title":"Mitigating Adversarial Effects Through Randomization","created":"2017-11-06","cited_by_count":383},{"id":"1901.09960","title":"Using Pre-Training Can Improve Model Robustness and Uncertainty","created":"2019-01-28","cited_by_count":373},{"id":"1612.07828","title":"Learning from Simulated and Unsupervised Images through Adversarial Training","created":"2016-12-22","cited_by_count":288},{"id":"2010.03593","title":"Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples","created":"2020-10-07","cited_by_count":278},{"id":"1711.05597","title":"Advances in Variational Inference","created":"2017-11-15","cited_by_count":236},{"id":"1607.01668","title":"Tensor Decomposition for Signal Processing and Machine Learning","created":"2016-07-06","cited_by_count":224},{"id":"1902.03932","title":"Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning","created":"2019-02-11","cited_by_count":203},{"id":"1803.01442","title":"Stochastic Activation Pruning for Robust Adversarial Defense","created":"2018-03-05","cited_by_count":171},{"id":"1302.4245","title":"Gaussian Process Kernels for Pattern Discovery and Extrapolation","created":"2013-02-18","cited_by_count":132},{"id":"1705.08665","title":"Bayesian Compression for Deep Learning","created":"2017-05-24","cited_by_count":116},{"id":"2002.08619","title":"Boosting Adversarial Training with Hypersphere Embedding","created":"2020-02-20","cited_by_count":97},{"id":"1302.4922","title":"Structure Discovery in Nonparametric Regression through Compositional Kernel Search","created":"2013-02-20","cited_by_count":88},{"id":"2010.01279","title":"Do Wider Neural Networks Really Help Adversarial Robustness?","created":"2020-10-03","cited_by_count":79},{"id":"1801.08926","title":"Deflecting Adversarial Attacks with Pixel Deflection","created":"2018-01-26","cited_by_count":71},{"id":"1401.6497","title":"Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination","created":"2014-01-25","cited_by_count":65},{"id":"1410.1390","title":"Convergence Analysis of Alternating Direction Method of Multipliers for a Family of Nonconvex Problems","created":"2014-10-06","cited_by_count":64},{"id":"1606.01500","title":"Efficient tensor completion for color image and video recovery: Low-rank tensor train","created":"2016-06-05","cited_by_count":61},{"id":"1705.10388","title":"Model Selection in Bayesian Neural Networks via Horseshoe Priors","created":"2017-05-29","cited_by_count":52},{"id":"2107.03436","title":"Tensor Methods in Computer Vision and Deep Learning","created":"2021-07-07","cited_by_count":52},{"id":"1710.07739","title":"Learning Discrete Weights Using the Local Reparameterization Trick","created":"2017-10-21","cited_by_count":29},{"id":"1705.08052","title":"Compressing Recurrent Neural Network with Tensor Train","created":"2017-05-23","cited_by_count":28},{"id":"1710.07324","title":"Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition","created":"2017-10-19","cited_by_count":28},{"id":"1905.10478","title":"Bayesian Tensorized Neural Networks with Automatic Rank Selection","created":"2019-05-24","cited_by_count":26},{"id":"1602.05221","title":"Patterns of Scalable Bayesian Inference","created":"2016-02-16","cited_by_count":25},{"id":"2008.00029","title":"Cold Posteriors and Aleatoric Uncertainty","created":"2020-07-31","cited_by_count":22},{"id":"1706.02524","title":"Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes","created":"2017-06-08","cited_by_count":19},{"id":"1805.07624","title":"Nonparametric Bayesian Deep Networks with Local Competition","created":"2018-05-19","cited_by_count":17},{"id":"2003.00605","title":"Stein Variational Inference for Discrete Distributions","created":"2020-03-01","cited_by_count":16},{"id":"1712.08048","title":"Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution","created":"2017-12-21","cited_by_count":15},{"id":"1804.06128","title":"Fast and Accurate Tensor Completion with Total Variation Regularized Tensor Trains","created":"2018-04-17","cited_by_count":14},{"id":"2101.01121","title":"Local Competition and Stochasticity for Adversarial Robustness in Deep Learning","created":"2021-01-04","cited_by_count":12},{"id":"2112.02671","title":"Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial Robustness","created":"2021-12-05","cited_by_count":11},{"id":"1904.09559","title":"Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Series","created":"2019-04-21","cited_by_count":9},{"id":"2009.02472","title":"Towards Flexible Sparsity-Aware Modeling: Automatic Tensor Rank Learning Using The Generalized Hyperbolic Prior","created":"2020-09-05","cited_by_count":9},{"id":"1505.02343","title":"Bayesian Sparse Tucker Models for Dimension Reduction and Tensor Completion","created":"2015-05-10","cited_by_count":8},{"id":"1902.04763","title":"Wireless Traffic Prediction with Scalable Gaussian Process: Framework, Algorithms, and Verification","created":"2019-02-13","cited_by_count":7},{"id":"2003.00474","title":"Scalable Learning Paradigms for Data-Driven Wireless Communication","created":"2020-03-01","cited_by_count":5},{"id":"2103.10134","title":"Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes: A Comprehensive Survey","created":"2021-03-18","cited_by_count":5},{"id":"1809.02153","title":"Variational Bayesian Inference for Robust Streaming Tensor Factorization and Completion","created":"2018-09-06","cited_by_count":4},{"id":"1711.02271","title":"High-order Tensor Completion for Data Recovery via Sparse Tensor-train Optimization","created":"2017-11-07","cited_by_count":3},{"id":"2101.09672","title":"Towards Overfitting Avoidance: Tuning-free Tensor-aided Multi-user Channel Estimation for 3D Massive MIMO Communications","created":"2021-01-24","cited_by_count":2}],"next_cursor":null}