{"list":"other","total":69,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1610.02391","title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization","created":"2016-10-07","cited_by_count":6733},{"id":"1608.06993","title":"Densely Connected Convolutional Networks","created":"2016-08-25","cited_by_count":6720},{"id":"1705.07874","title":"A Unified Approach to Interpreting Model Predictions","created":"2017-05-22","cited_by_count":6555},{"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":"1607.00133","title":"Deep Learning with Differential Privacy","created":"2016-07-01","cited_by_count":2728},{"id":"1312.6034","title":"Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps","created":"2013-12-20","cited_by_count":2371},{"id":"1610.05820","title":"Membership Inference Attacks against Machine Learning Models","created":"2016-10-18","cited_by_count":2281},{"id":"1905.00414","title":"Similarity of Neural Network Representations Revisited","created":"2019-05-01","cited_by_count":1755},{"id":"1810.03292","title":"Sanity Checks for Saliency Maps","created":"2018-10-08","cited_by_count":1156},{"id":"2104.00298","title":"EfficientNetV2: Smaller Models and Faster Training","created":"2021-04-01","cited_by_count":1135},{"id":"1706.03825","title":"SmoothGrad: removing noise by adding noise","created":"2017-06-12","cited_by_count":1109},{"id":"1806.07421","title":"RISE: Randomized Input Sampling for Explanation of Black-box Models","created":"2018-06-19","cited_by_count":838},{"id":"2004.03685","title":"Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?","created":"2020-04-07","cited_by_count":599},{"id":"2009.07896","title":"Captum: A unified and generic model interpretability library for PyTorch","created":"2020-09-16","cited_by_count":547},{"id":"1509.06321","title":"Evaluating the visualization of what a Deep Neural Network has learned","created":"2015-09-21","cited_by_count":419},{"id":"2109.12298","title":"Opacus: User-Friendly Differential Privacy Library in PyTorch","created":"2021-09-25","cited_by_count":383},{"id":"1908.08474","title":"The many Shapley values for model explanation","created":"2019-08-22","cited_by_count":344},{"id":"1608.00507","title":"Top-down Neural Attention by Excitation Backprop","created":"2016-08-01","cited_by_count":321},{"id":"2103.07853","title":"Membership Inference Attacks on Machine Learning: A Survey","created":"2021-03-14","cited_by_count":321},{"id":"1806.08049","title":"On the Robustness of Interpretability Methods","created":"2018-06-21","cited_by_count":314},{"id":"1901.09392","title":"On the (In)fidelity and Sensitivity for Explanations","created":"2019-01-27","cited_by_count":310},{"id":"1802.08908","title":"Scalable Private Learning with PATE","created":"2018-02-24","cited_by_count":269},{"id":"2012.15029","title":"VinDr-CXR: An open dataset of chest X-rays with radiologist's annotations","created":"2020-12-30","cited_by_count":239},{"id":"1909.06342","title":"Explainable Machine Learning in Deployment","created":"2019-09-13","cited_by_count":211},{"id":"1708.08296","title":"Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models","created":"2017-08-28","cited_by_count":206},{"id":"2002.11097","title":"Problems with Shapley-value-based explanations as feature importance measures","created":"2020-02-25","cited_by_count":200},{"id":"2202.06861","title":"Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond","created":"2022-02-14","cited_by_count":171},{"id":"2202.01602","title":"The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective","created":"2022-02-03","cited_by_count":159},{"id":"2303.00654","title":"How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy","created":"2023-03-01","cited_by_count":153},{"id":"2007.07646","title":"A Survey of Privacy Attacks in Machine Learning","created":"2020-07-15","cited_by_count":146},{"id":"1807.08024","title":"Explaining Image Classifiers by Counterfactual Generation","created":"2018-07-20","cited_by_count":144},{"id":"2305.06329","title":"Similarity of Neural Network Models: A Survey of Functional and Representational Measures","created":"2023-05-10","cited_by_count":131},{"id":"2209.11326","title":"Towards Faithful Model Explanation in NLP: A Survey","created":"2022-09-22","cited_by_count":130},{"id":"2111.06420","title":"Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities","created":"2021-11-11","cited_by_count":125},{"id":"2002.04138","title":"Explaining Explanations: Axiomatic Feature Interactions for Deep Networks","created":"2020-02-10","cited_by_count":103},{"id":"1911.12116","title":"Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey","created":"2019-11-27","cited_by_count":93},{"id":"2106.00786","title":"The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations","created":"2021-06-01","cited_by_count":87},{"id":"2210.16156","title":"Reliability of CKA as a Similarity Measure in Deep Learning","created":"2022-10-28","cited_by_count":87},{"id":"2006.01067","title":"Aligning Faithful Interpretations with their Social Attribution","created":"2020-06-01","cited_by_count":79},{"id":"2201.10787","title":"Variational Model Inversion Attacks","created":"2022-01-26","cited_by_count":75},{"id":"2206.08451","title":"I Know What You Trained Last Summer: A Survey on Stealing Machine Learning Models and Defences","created":"2022-06-16","cited_by_count":75},{"id":"2007.07584","title":"On quantitative aspects of model interpretability","created":"2020-07-15","cited_by_count":71},{"id":"2010.09063","title":"Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization","created":"2020-10-18","cited_by_count":58},{"id":"1807.04644","title":"Algorithms that Remember: Model Inversion Attacks and Data Protection Law","created":"2018-07-12","cited_by_count":55},{"id":"2202.00734","title":"Framework for Evaluating Faithfulness of Local Explanations","created":"2022-02-01","cited_by_count":55},{"id":"2007.14895","title":"Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization","created":"2020-07-29","cited_by_count":50},{"id":"2202.08187","title":"Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey","created":"2022-02-16","cited_by_count":47},{"id":"2003.08747","title":"IROF: a low resource evaluation metric for explanation methods","created":"2020-03-09","cited_by_count":46},{"id":"2010.06667","title":"Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings","created":"2020-10-13","cited_by_count":37},{"id":"2104.14995","title":"Interpretable Semantic Photo Geolocation","created":"2021-04-30","cited_by_count":32},{"id":"2005.08874","title":"Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps","created":"2020-05-18","cited_by_count":30},{"id":"2101.00905","title":"On Baselines for Local Feature Attributions","created":"2021-01-04","cited_by_count":29},{"id":"1906.02004","title":"Interpretable and Differentially Private Predictions","created":"2019-06-05","cited_by_count":28},{"id":"2206.04621","title":"A Critical Review on the Use (and Misuse) of Differential Privacy in Machine Learning","created":"2022-06-09","cited_by_count":27},{"id":"2403.01759","title":"Open-world machine learning: A review and new outlooks","created":"2024-03-04","cited_by_count":27},{"id":"2006.09129","title":"Model Explanations with Differential Privacy","created":"2020-06-16","cited_by_count":23},{"id":"2110.14297","title":"Revisiting Sanity Checks for Saliency Maps","created":"2021-10-27","cited_by_count":20},{"id":"2310.20360","title":"Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory","created":"2023-10-31","cited_by_count":20},{"id":"2202.00095","title":"Deconfounded Representation Similarity for Comparison of Neural Networks","created":"2022-01-31","cited_by_count":19},{"id":"2109.05463","title":"Logic Traps in Evaluating Attribution Scores","created":"2021-09-12","cited_by_count":18},{"id":"2012.07828","title":"Robustness Threats of Differential Privacy","created":"2020-12-14","cited_by_count":15},{"id":"2208.02878","title":"Differentially Private Counterfactuals via Functional Mechanism","created":"2022-08-04","cited_by_count":13},{"id":"2402.09540","title":"Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks?","created":"2024-02-14","cited_by_count":12},{"id":"2106.07475","title":"Investigating sanity checks for saliency maps with image and text classification","created":"2021-06-08","cited_by_count":11},{"id":"2211.04110","title":"Privacy Meets Explainability: A Comprehensive Impact Benchmark","created":"2022-11-08","cited_by_count":9},{"id":"2404.11428","title":"Explainable Lung Disease Classification from Chest X-Ray Images Utilizing Deep Learning and XAI","created":"2024-04-17","cited_by_count":3},{"id":"2406.17298","title":"Efficient and Scalable Implementation of Differentially Private Deep Learning without Shortcuts","created":"2024-06-25","cited_by_count":3}],"next_cursor":null}