{"list":"other","total":53,"rows":[{"id":"1610.02391","title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization","created":"2016-10-07","cited_by_count":6733},{"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":"1312.6034","title":"Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps","created":"2013-12-20","cited_by_count":2371},{"id":"1704.02685","title":"Learning Important Features Through Propagating Activation Differences","created":"2017-04-10","cited_by_count":1458},{"id":"1711.11279","title":"Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)","created":"2017-11-30","cited_by_count":1319},{"id":"1706.03825","title":"SmoothGrad: removing noise by adding noise","created":"2017-06-12","cited_by_count":1109},{"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":"1704.05796","title":"Network Dissection: Quantifying Interpretability of Deep Visual Representations","created":"2017-04-19","cited_by_count":708},{"id":"1604.04173","title":"Distribution-Free Predictive Inference For Regression","created":"2016-04-14","cited_by_count":668},{"id":"1704.03296","title":"Interpretable Explanations of Black Boxes by Meaningful Perturbation","created":"2017-04-11","cited_by_count":612},{"id":"1911.02508","title":"Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods","created":"2019-11-06","cited_by_count":443},{"id":"1801.01489","title":"All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously","created":"2018-01-04","cited_by_count":373},{"id":"1606.00931","title":"DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network","created":"2016-06-02","cited_by_count":358},{"id":"2004.13912","title":"Neural Additive Models: Interpretable Machine Learning with Neural Nets","created":"2020-04-29","cited_by_count":319},{"id":"1612.08468","title":"Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models","created":"2016-12-27","cited_by_count":213},{"id":"1903.10464","title":"Explaining individual predictions when features are dependent: More accurate approximations to Shapley values","created":"2019-03-25","cited_by_count":208},{"id":"1508.04409","title":"ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R","created":"2015-08-18","cited_by_count":172},{"id":"2004.11165","title":"Multi-Objective Counterfactual Explanations","created":"2020-04-23","cited_by_count":157},{"id":"2010.09337","title":"Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges","created":"2020-10-19","cited_by_count":120},{"id":"1708.04649","title":"Machine Learning for Survival Analysis: A Survey","created":"2017-08-15","cited_by_count":112},{"id":"1905.03151","title":"Unrestricted Permutation forces Extrapolation: Variable Importance Requires at least One More Model, or There Is No Free Variable Importance","created":"2019-05-01","cited_by_count":87},{"id":"2007.04131","title":"General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models","created":"2020-07-08","cited_by_count":75},{"id":"2305.14961","title":"Deep Learning for Survival Analysis: A Review","created":"2023-05-24","cited_by_count":75},{"id":"2203.00870","title":"Faith-Shap: The Faithful Shapley Interaction Index","created":"2022-03-02","cited_by_count":69},{"id":"2106.07875","title":"S-LIME: Stabilized-LIME for Model Explanation","created":"2021-06-15","cited_by_count":52},{"id":"2110.00855","title":"SurvTRACE: Transformers for Survival Analysis with Competing Events","created":"2021-10-02","cited_by_count":41},{"id":"2003.08371","title":"SurvLIME: A method for explaining machine learning survival models","created":"2020-03-18","cited_by_count":34},{"id":"1901.09917","title":"Testing Conditional Independence in Supervised Learning Algorithms","created":"2019-01-28","cited_by_count":27},{"id":"2109.01433","title":"Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process","created":"2021-09-03","cited_by_count":24},{"id":"2208.11080","title":"SurvSHAP(t): Time-dependent explanations of machine learning survival models","created":"2022-08-23","cited_by_count":24},{"id":"2304.04780","title":"A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?","created":"2023-04-10","cited_by_count":24},{"id":"2206.05487","title":"Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena","created":"2022-06-11","cited_by_count":21},{"id":"2111.13507","title":"Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features","created":"2021-11-26","cited_by_count":19},{"id":"2208.06151","title":"Unifying local and global model explanations by functional decomposition of low dimensional structures","created":"2022-08-12","cited_by_count":18},{"id":"2104.08903","title":"SurvNAM: The machine learning survival model explanation","created":"2021-04-18","cited_by_count":16},{"id":"1908.02337","title":"DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values","created":"2019-08-06","cited_by_count":15},{"id":"2305.09536","title":"A Comparative Study of Methods for Estimating Conditional Shapley Values and When to Use Them","created":"2023-05-16","cited_by_count":15},{"id":"2003.03621","title":"Large-scale benchmark study of survival prediction methods using multi-omics data","created":"2020-03-07","cited_by_count":14},{"id":"1909.03890","title":"A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data","created":"2019-09-09","cited_by_count":12},{"id":"2202.07423","title":"DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis","created":"2022-02-12","cited_by_count":12},{"id":"2206.10477","title":"Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee","created":"2022-06-21","cited_by_count":9},{"id":"2006.16793","title":"Counterfactual explanation of machine learning survival models","created":"2020-06-26","cited_by_count":8},{"id":"2005.02249","title":"A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds","created":"2020-05-05","cited_by_count":7},{"id":"2005.02387","title":"SurvLIME-Inf: A simplified modification of SurvLIME for explanation of machine learning survival models","created":"2020-05-05","cited_by_count":7},{"id":"2210.03047","title":"Conditional Feature Importance for Mixed Data","created":"2022-10-06","cited_by_count":7},{"id":"2302.10571","title":"SurvLIMEpy: A Python package implementing SurvLIME","created":"2023-02-21","cited_by_count":4},{"id":"2308.03730","title":"SurvBeX: An explanation method of the machine learning survival models based on the Beran estimator","created":"2023-08-07","cited_by_count":3},{"id":"2012.08649","title":"Effect of right censoring bias on survival analysis","created":"2020-12-15","cited_by_count":2},{"id":"2402.00072","title":"Explainable AI for survival analysis: a median-SHAP approach","created":"2024-01-30","cited_by_count":2},{"id":"2308.16113","title":"survex: an R package for explaining machine learning survival models","created":"2023-08-30","cited_by_count":1}],"next_cursor":null}