{"list":"other","total":55,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"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":"1903.12261","title":"Benchmarking Neural Network Robustness to Common Corruptions and Perturbations","created":"2019-03-28","cited_by_count":2877},{"id":"1411.1784","title":"Conditional Generative Adversarial Nets","created":"2014-11-06","cited_by_count":2162},{"id":"1907.02893","title":"Invariant Risk Minimization","created":"2019-07-05","cited_by_count":1854},{"id":"2006.16241","title":"The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization","created":"2020-06-29","cited_by_count":1850},{"id":"1901.08573","title":"Theoretically Principled Trade-off between Robustness and Accuracy","created":"2019-01-24","cited_by_count":1517},{"id":"2012.07421","title":"WILDS: A Benchmark of in-the-Wild Distribution Shifts","created":"2020-12-14","cited_by_count":1375},{"id":"1902.02918","title":"Certified Adversarial Robustness via Randomized Smoothing","created":"2019-02-08","cited_by_count":1340},{"id":"1511.04599","title":"DeepFool: a simple and accurate method to fool deep neural networks","created":"2015-11-14","cited_by_count":1318},{"id":"1804.04732","title":"Multimodal Unsupervised Image-to-Image Translation","created":"2018-04-12","cited_by_count":795},{"id":"1702.01135","title":"Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks","created":"2017-02-03","cited_by_count":744},{"id":"1708.06131","title":"Evasion Attacks against Machine Learning at Test Time","created":"2017-08-21","cited_by_count":689},{"id":"1711.00851","title":"Provable defenses against adversarial examples via the convex outer adversarial polytope","created":"2017-11-02","cited_by_count":567},{"id":"1811.00866","title":"Efficient Neural Network Robustness Certification with General Activation Functions","created":"2018-11-02","cited_by_count":445},{"id":"2106.07594","title":"Graph Contrastive Learning Automated","created":"2021-06-10","cited_by_count":287},{"id":"1610.06940","title":"Safety Verification of Deep Neural Networks","created":"2016-10-21","cited_by_count":263},{"id":"1804.09699","title":"Towards Fast Computation of Certified Robustness for ReLU Networks","created":"2018-04-25","cited_by_count":255},{"id":"1705.01320","title":"Formal Verification of Piece-Wise Linear Feed-Forward Neural Networks","created":"2017-05-03","cited_by_count":242},{"id":"1803.06373","title":"Adversarial Logit Pairing","created":"2018-03-16","cited_by_count":224},{"id":"2011.13824","title":"Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers","created":"2020-11-27","cited_by_count":187},{"id":"1804.10829","title":"Formal Security Analysis of Neural Networks using Symbolic Intervals","created":"2018-04-28","cited_by_count":185},{"id":"1811.01057","title":"Semidefinite relaxations for certifying robustness to adversarial examples","created":"2018-11-02","cited_by_count":179},{"id":"1809.08098","title":"Efficient Formal Safety Analysis of Neural Networks","created":"2018-09-19","cited_by_count":165},{"id":"1909.06588","title":"Branch and Bound for Piecewise Linear Neural Network Verification","created":"2019-09-14","cited_by_count":138},{"id":"1803.06567","title":"A Dual Approach to Scalable Verification of Deep Networks","created":"2018-03-17","cited_by_count":125},{"id":"1902.08722","title":"A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks","created":"2019-02-23","cited_by_count":125},{"id":"2102.11436","title":"Model-Based Domain Generalization","created":"2021-02-23","cited_by_count":104},{"id":"2205.00263","title":"Complete Verification via Multi-Neuron Relaxation Guided Branch-and-Bound","created":"2022-04-30","cited_by_count":92},{"id":"1811.01828","title":"Verisig: verifying safety properties of hybrid systems with neural network controllers","created":"2018-11-05","cited_by_count":84},{"id":"1708.03322","title":"Output Reachable Set Estimation and Verification for Multi-Layer Neural Networks","created":"2017-08-09","cited_by_count":80},{"id":"2103.03638","title":"PRIMA: General and Precise Neural Network Certification via Scalable Convex Hull Approximations","created":"2021-03-05","cited_by_count":77},{"id":"2004.05511","title":"Verification of Deep Convolutional Neural Networks Using ImageStars","created":"2020-04-12","cited_by_count":75},{"id":"1910.14574","title":"An Abstraction-Based Framework for Neural Network Verification","created":"2019-10-31","cited_by_count":68},{"id":"1404.1869","title":"DenseNet: Implementing Efficient ConvNet Descriptor Pyramids","created":"2014-04-07","cited_by_count":66},{"id":"1811.12395","title":"CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks","created":"2018-11-29","cited_by_count":50},{"id":"2006.14076","title":"The Convex Relaxation Barrier, Revisited: Tightened Single-Neuron Relaxations for Neural Network Verification","created":"2020-06-24","cited_by_count":50},{"id":"2007.08450","title":"Learning perturbation sets for robust machine learning","created":"2020-07-16","cited_by_count":48},{"id":"1904.09959","title":"Optimization and Abstraction: A Synergistic Approach for Analyzing Neural Network Robustness","created":"2019-04-22","cited_by_count":40},{"id":"1912.00574","title":"Fastened CROWN: Tightened Neural Network Robustness Certificates","created":"2019-12-02","cited_by_count":39},{"id":"2110.15767","title":"Adversarial Robustness with Semi-Infinite Constrained Learning","created":"2021-10-29","cited_by_count":39},{"id":"2108.01220","title":"OVERT: An Algorithm for Safety Verification of Neural Network Control Policies for Nonlinear Systems","created":"2021-08-03","cited_by_count":38},{"id":"2002.10410","title":"Lagrangian Decomposition for Neural Network Verification","created":"2020-02-24","cited_by_count":35},{"id":"1912.03192","title":"Achieving Robustness in the Wild via Adversarial Mixing with Disentangled Representations","created":"2019-12-06","cited_by_count":28},{"id":"2005.10247","title":"Model-Based Robust Deep Learning: Generalizing to Natural, Out-of-Distribution Data","created":"2020-05-20","cited_by_count":27},{"id":"2105.07091","title":"Verification of Image-based Neural Network Controllers Using Generative Models","created":"2021-05-14","cited_by_count":27},{"id":"1712.06174","title":"Deep Neural Networks as 0-1 Mixed Integer Linear Programs: A Feasibility Study","created":"2017-12-17","cited_by_count":25},{"id":"2203.11201","title":"Efficient Neural Network Analysis with Sum-of-Infeasibilities","created":"2022-03-19","cited_by_count":24},{"id":"2203.00938","title":"Neuro-Symbolic Verification of Deep Neural Networks","created":"2022-03-02","cited_by_count":21},{"id":"2005.13300","title":"Scalable Polyhedral Verification of Recurrent Neural Networks","created":"2020-05-27","cited_by_count":20},{"id":"2203.03153","title":"Scalable Verification of GNN-based Job Schedulers","created":"2022-03-07","cited_by_count":19},{"id":"2103.01629","title":"DeepCert: Verification of Contextually Relevant Robustness for Neural Network Image Classifiers","created":"2021-03-02","cited_by_count":11},{"id":"1912.09533","title":"Towards Verifying Robustness of Neural Networks Against Semantic Perturbations","created":"2019-12-19","cited_by_count":3},{"id":"2101.06825","title":"Counterexample-Guided Prophecy for Model Checking Modulo the Theory of Arrays","created":"2021-01-18","cited_by_count":2}],"next_cursor":null}