{"list":"other","total":106,"rows":[{"id":"1409.0575","title":"ImageNet Large Scale Visual Recognition Challenge","created":"2014-09-01","cited_by_count":8498},{"id":"1412.6572","title":"Explaining and Harnessing Adversarial Examples","created":"2014-12-20","cited_by_count":7364},{"id":"1705.07874","title":"A Unified Approach to Interpreting Model Predictions","created":"2017-05-22","cited_by_count":6555},{"id":"1706.06083","title":"Towards Deep Learning Models Resistant to Adversarial Attacks","created":"2017-06-19","cited_by_count":6415},{"id":"1312.6199","title":"Intriguing properties of neural networks","created":"2013-12-21","cited_by_count":5155},{"id":"1602.04938","title":"\"Why Should I Trust You?\": Explaining the Predictions of Any Classifier","created":"2016-02-16","cited_by_count":5070},{"id":"1703.01365","title":"Axiomatic Attribution for Deep Networks","created":"2017-03-04","cited_by_count":3384},{"id":"1608.04644","title":"Towards Evaluating the Robustness of Neural Networks","created":"2016-08-16","cited_by_count":2998},{"id":"1312.6034","title":"Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps","created":"2013-12-20","cited_by_count":2371},{"id":"1311.2901","title":"Visualizing and Understanding Convolutional Networks","created":"2013-11-12","cited_by_count":2290},{"id":"1703.04730","title":"Understanding Black-box Predictions via Influence Functions","created":"2017-03-14","cited_by_count":1741},{"id":"1702.08608","title":"Towards A Rigorous Science of Interpretable Machine Learning","created":"2017-02-28","cited_by_count":1416},{"id":"1711.11279","title":"Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)","created":"2017-11-30","cited_by_count":1319},{"id":"1511.04599","title":"DeepFool: a simple and accurate method to fool deep neural networks","created":"2015-11-14","cited_by_count":1318},{"id":"1711.00399","title":"Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR","created":"2017-11-01","cited_by_count":1089},{"id":"1412.6806","title":"Striving for Simplicity: The All Convolutional Net","created":"2014-12-21","cited_by_count":990},{"id":"1602.02697","title":"Practical Black-Box Attacks against Machine Learning","created":"2016-02-08","cited_by_count":903},{"id":"1805.12152","title":"Robustness May Be at Odds with Accuracy","created":"2018-05-30","cited_by_count":880},{"id":"1610.08401","title":"Universal adversarial perturbations","created":"2016-10-26","cited_by_count":784},{"id":"1806.10574","title":"This Looks Like That: Deep Learning for Interpretable Image Recognition","created":"2018-06-27","cited_by_count":737},{"id":"1606.03490","title":"The Mythos of Model Interpretability","created":"2016-06-10","cited_by_count":721},{"id":"1708.03999","title":"ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models","created":"2017-08-14","cited_by_count":666},{"id":"1611.02770","title":"Delving into Transferable Adversarial Examples and Black-box Attacks","created":"2016-11-08","cited_by_count":536},{"id":"1806.00069","title":"Explaining Explanations: An Overview of Interpretability of Machine Learning","created":"2018-05-31","cited_by_count":508},{"id":"1806.07538","title":"Towards Robust Interpretability with Self-Explaining Neural Networks","created":"2018-06-20","cited_by_count":505},{"id":"1804.08598","title":"Black-box Adversarial Attacks with Limited Queries and Information","created":"2018-04-23","cited_by_count":501},{"id":"1712.04248","title":"Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models","created":"2017-12-12","cited_by_count":462},{"id":"1710.10547","title":"Interpretation of Neural Networks is Fragile","created":"2017-10-29","cited_by_count":444},{"id":"1911.02508","title":"Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods","created":"2019-11-06","cited_by_count":443},{"id":"1712.07107","title":"Adversarial Examples: Attacks and Defenses for Deep Learning","created":"2017-12-19","cited_by_count":351},{"id":"1506.06579","title":"Understanding Neural Networks Through Deep Visualization","created":"2015-06-22","cited_by_count":334},{"id":"1711.00867","title":"The (Un)reliability of saliency methods","created":"2017-11-02","cited_by_count":333},{"id":"1809.06514","title":"Actionable Recourse in Linear Classification","created":"2018-09-18","cited_by_count":328},{"id":"1806.08049","title":"On the Robustness of Interpretability Methods","created":"2018-06-21","cited_by_count":314},{"id":"1711.02173","title":"Synthetic and Natural Noise Both Break Neural Machine Translation","created":"2017-11-06","cited_by_count":306},{"id":"1703.03717","title":"Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations","created":"2017-03-10","cited_by_count":294},{"id":"2012.14261","title":"A Survey on Neural Network Interpretability","created":"2020-12-28","cited_by_count":291},{"id":"1703.08603","title":"Adversarial Examples for Semantic Segmentation and Object Detection","created":"2017-03-24","cited_by_count":275},{"id":"1904.08653","title":"Fooling automated surveillance cameras: adversarial patches to attack person detection","created":"2019-04-18","cited_by_count":268},{"id":"2003.09871","title":"COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images","created":"2020-03-22","cited_by_count":264},{"id":"1711.09404","title":"Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients","created":"2017-11-26","cited_by_count":236},{"id":"1710.04806","title":"Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions","created":"2017-10-13","cited_by_count":234},{"id":"2002.01650","title":"Concept Whitening for Interpretable Image Recognition","created":"2020-02-05","cited_by_count":225},{"id":"1906.07983","title":"Explanations can be manipulated and geometry is to blame","created":"2019-06-19","cited_by_count":216},{"id":"2012.02046","title":"Neural Prototype Trees for Interpretable Fine-grained Image Recognition","created":"2020-12-03","cited_by_count":214},{"id":"1811.09720","title":"Representer Point Selection for Explaining Deep Neural Networks","created":"2018-11-23","cited_by_count":187},{"id":"1710.11342","title":"Generating Natural Adversarial Examples","created":"2017-10-31","cited_by_count":183},{"id":"1703.06748","title":"Tactics of Adversarial Attack on Deep Reinforcement Learning Agents","created":"2017-03-08","cited_by_count":166},{"id":"1710.00935","title":"Interpretable Convolutional Neural Networks","created":"2017-10-02","cited_by_count":162},{"id":"1712.02328","title":"Generative Adversarial Perturbations","created":"2017-12-06","cited_by_count":153},{"id":"1907.10456","title":"Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems","created":"2019-07-24","cited_by_count":153},{"id":"2001.05371","title":"Making deep neural networks right for the right scientific reasons by interacting with their explanations","created":"2020-01-15","cited_by_count":142},{"id":"2004.14545","title":"Explainable Deep Learning: A Field Guide for the Uninitiated","created":"2020-04-30","cited_by_count":127},{"id":"2106.02666","title":"Counterfactual Explanations Can Be Manipulated","created":"2021-06-04","cited_by_count":113},{"id":"1902.02041","title":"Fooling Neural Network Interpretations via Adversarial Model Manipulation","created":"2019-02-06","cited_by_count":112},{"id":"1911.06473","title":"\"How do I fool you?\": Manipulating User Trust via Misleading Black Box Explanations","created":"2019-11-15","cited_by_count":111},{"id":"1906.02443","title":"Robust Neural Machine Translation with Doubly Adversarial Inputs","created":"2019-06-06","cited_by_count":106},{"id":"1803.01128","title":"Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples","created":"2018-03-03","cited_by_count":94},{"id":"1704.05712","title":"Universal Adversarial Perturbations Against Semantic Image Segmentation","created":"2017-04-19","cited_by_count":92},{"id":"2005.11295","title":"From ImageNet to Image Classification: Contextualizing Progress on Benchmarks","created":"2020-05-22","cited_by_count":90},{"id":"1801.08092","title":"Generalizable Data-free Objective for Crafting Universal Adversarial Perturbations","created":"2018-01-24","cited_by_count":88},{"id":"2105.14944","title":"The effectiveness of feature attribution methods and its correlation with automatic evaluation scores","created":"2021-05-31","cited_by_count":88},{"id":"1806.09030","title":"On Adversarial Examples for Character-Level Neural Machine Translation","created":"2018-06-23","cited_by_count":85},{"id":"1805.11090","title":"GenAttack: Practical Black-box Attacks with Gradient-Free Optimization","created":"2018-05-28","cited_by_count":82},{"id":"1901.09749","title":"Fairwashing: the risk of rationalization","created":"2019-01-28","cited_by_count":81},{"id":"1905.04172","title":"On the Connection Between Adversarial Robustness and Saliency Map Interpretability","created":"2019-05-10","cited_by_count":80},{"id":"1905.09797","title":"Interpreting Adversarially Trained Convolutional Neural Networks","created":"2019-05-23","cited_by_count":75},{"id":"2002.08596","title":"Interpretability of machine learning based prediction models in healthcare","created":"2020-02-20","cited_by_count":75},{"id":"1812.00891","title":"Interpretable Deep Learning under Fire","created":"2018-12-03","cited_by_count":74},{"id":"1707.05572","title":"Fast Feature Fool: A data independent approach to universal adversarial perturbations","created":"2017-07-18","cited_by_count":66},{"id":"1906.10651","title":"Interpretable Image Recognition with Hierarchical Prototypes","created":"2019-06-25","cited_by_count":65},{"id":"1702.06832","title":"Adversarial examples for generative models","created":"2017-02-22","cited_by_count":58},{"id":"1903.06620","title":"On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models","created":"2019-03-15","cited_by_count":56},{"id":"1709.03582","title":"Art of singular vectors and universal adversarial perturbations","created":"2017-09-11","cited_by_count":49},{"id":"1803.07517","title":"Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges","created":"2018-03-20","cited_by_count":49},{"id":"1906.03499","title":"ML-LOO: Detecting Adversarial Examples with Feature Attribution","created":"2019-06-08","cited_by_count":48},{"id":"1905.09957","title":"Robust Attribution Regularization","created":"2019-05-23","cited_by_count":47},{"id":"2101.08030","title":"Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data","created":"2021-01-20","cited_by_count":45},{"id":"2004.04199","title":"Transferable, Controllable, and Inconspicuous Adversarial Attacks on Person Re-identification With Deep Mis-Ranking","created":"2020-04-08","cited_by_count":42},{"id":"2006.14748","title":"Proper Network Interpretability Helps Adversarial Robustness in Classification","created":"2020-06-26","cited_by_count":41},{"id":"2010.05419","title":"Gradient-based Analysis of NLP Models is Manipulable","created":"2020-10-12","cited_by_count":41},{"id":"2006.06356","title":"Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored Factors","created":"2020-06-11","cited_by_count":40},{"id":"1810.11580","title":"Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples","created":"2018-10-27","cited_by_count":38},{"id":"2006.06643","title":"Smoothed Geometry for Robust Attribution","created":"2020-06-11","cited_by_count":38},{"id":"1911.03274","title":"Imperceptible Adversarial Attacks on Tabular Data","created":"2019-11-08","cited_by_count":35},{"id":"1703.01101","title":"Adversarial Examples for Semantic Image Segmentation","created":"2017-03-03","cited_by_count":33},{"id":"1808.01153","title":"Ask, Acquire, and Attack: Data-free UAP Generation using Class Impressions","created":"2018-08-03","cited_by_count":31},{"id":"2009.11911","title":"Adversarial Examples in Deep Learning for Multivariate Time Series Regression","created":"2020-09-24","cited_by_count":26},{"id":"2106.14648","title":"On Locality of Local Explanation Models","created":"2021-06-24","cited_by_count":25},{"id":"2106.08361","title":"Adversarial Attacks on Deep Models for Financial Transaction Records","created":"2021-06-15","cited_by_count":24},{"id":"2007.06381","title":"A simple defense against adversarial attacks on heatmap explanations","created":"2020-07-13","cited_by_count":23},{"id":"2106.07504","title":"Characterizing the risk of fairwashing","created":"2021-06-14","cited_by_count":21},{"id":"1612.00155","title":"Adversarial Images for Variational Autoencoders","created":"2016-12-01","cited_by_count":19},{"id":"1906.10671","title":"Explaining Deep Learning Models with Constrained Adversarial Examples","created":"2019-06-25","cited_by_count":19},{"id":"1807.04178","title":"Explainable Security","created":"2018-07-11","cited_by_count":15},{"id":"2001.07641","title":"Deceptive AI Explanations: Creation and Detection","created":"2020-01-21","cited_by_count":14},{"id":"1905.12282","title":"CopyCAT: Taking Control of Neural Policies with Constant Attacks","created":"2019-05-29","cited_by_count":12},{"id":"2104.15064","title":"Black-box adversarial attacks using Evolution Strategies","created":"2021-04-30","cited_by_count":11},{"id":"1807.02905","title":"Vulnerability Analysis of Chest X-Ray Image Classification Against Adversarial Attacks","created":"2018-07-09","cited_by_count":9},{"id":"1803.08773","title":"Detecting Adversarial Perturbations with Saliency","created":"2018-03-23","cited_by_count":8}],"next_cursor":"100"}