{"list":"other","total":61,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1602.04938","title":"\"Why Should I Trust You?\": Explaining the Predictions of Any Classifier","created":"2016-02-16","cited_by_count":5070},{"id":"1806.07366","title":"Neural Ordinary Differential Equations","created":"2018-06-19","cited_by_count":3779},{"id":"1907.02893","title":"Invariant Risk Minimization","created":"2019-07-05","cited_by_count":1854},{"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":"1810.03292","title":"Sanity Checks for Saliency Maps","created":"2018-10-08","cited_by_count":1156},{"id":"2007.04612","title":"Concept Bottleneck Models","created":"2020-07-09","cited_by_count":1032},{"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":"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":"1905.07697","title":"Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations","created":"2019-05-19","cited_by_count":614},{"id":"1501.01332","title":"Causal inference using invariant prediction: identification and confidence intervals","created":"2015-01-06","cited_by_count":536},{"id":"1911.01429","title":"The frontier of simulation-based inference","created":"2019-11-04","cited_by_count":502},{"id":"1706.03461","title":"Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning","created":"2017-06-12","cited_by_count":478},{"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":"2001.00973","title":"Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing","created":"2020-01-03","cited_by_count":384},{"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":"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":"2006.14032","title":"Compositional Explanations of Neurons","created":"2020-06-24","cited_by_count":172},{"id":"1910.13413","title":"Feature relevance quantification in explainable AI: A causal problem","created":"2019-10-29","cited_by_count":167},{"id":"1905.08883","title":"Explainable Machine Learning for Scientific Insights and Discoveries","created":"2019-05-21","cited_by_count":166},{"id":"1512.05287","title":"A Theoretically Grounded Application of Dropout in Recurrent Neural Networks","created":"2015-12-16","cited_by_count":161},{"id":"2202.01602","title":"The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective","created":"2022-02-03","cited_by_count":159},{"id":"2004.11165","title":"Multi-Objective Counterfactual Explanations","created":"2020-04-23","cited_by_count":157},{"id":"2006.16234","title":"True to the Model or True to the Data?","created":"2020-06-29","cited_by_count":126},{"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":"1610.02995","title":"Extrapolation and learning equations","created":"2016-10-10","cited_by_count":110},{"id":"1602.03616","title":"Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks","created":"2016-02-11","cited_by_count":106},{"id":"1910.12336","title":"CXPlain: Causal Explanations for Model Interpretation under Uncertainty","created":"2019-10-27","cited_by_count":98},{"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":"2002.00772","title":"Evaluating Saliency Map Explanations for Convolutional Neural Networks: A User Study","created":"2020-02-03","cited_by_count":86},{"id":"2007.04131","title":"General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models","created":"2020-07-08","cited_by_count":75},{"id":"1804.06620","title":"Visualizing the Feature Importance for Black Box Models","created":"2018-04-18","cited_by_count":55},{"id":"2010.14592","title":"Shapley Flow: A Graph-based Approach to Interpreting Model Predictions","created":"2020-10-27","cited_by_count":51},{"id":"1302.4973","title":"Strong Completeness and Faithfulness in Bayesian Networks","created":"2013-02-20","cited_by_count":45},{"id":"1503.07810","title":"Interpretable Classification Models for Recidivism Prediction","created":"2015-03-26","cited_by_count":44},{"id":"2103.08951","title":"Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties","created":"2021-03-16","cited_by_count":41},{"id":"2106.14421","title":"Causal Reinforcement Learning using Observational and Interventional Data","created":"2021-06-28","cited_by_count":39},{"id":"2006.04628","title":"Model-agnostic Feature Importance and Effects with Dependent Features -- A Conditional Subgroup Approach","created":"2020-06-08","cited_by_count":37},{"id":"2111.04820","title":"Explaining Hyperparameter Optimization via Partial Dependence Plots","created":"2021-11-08","cited_by_count":35},{"id":"1806.02891","title":"Revisiting the Importance of Individual Units in CNNs via Ablation","created":"2018-06-07","cited_by_count":32},{"id":"1911.01850","title":"Stabilizing Variable Selection and Regression","created":"2019-11-05","cited_by_count":31},{"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":"1907.08982","title":"Noise Regularization for Conditional Density Estimation","created":"2019-07-21","cited_by_count":23},{"id":"2202.04513","title":"The no-free-lunch theorems of supervised learning","created":"2022-02-09","cited_by_count":16},{"id":"2003.01262","title":"Selectivity considered harmful: evaluating the causal impact of class selectivity in DNNs","created":"2020-03-03","cited_by_count":15},{"id":"2111.10853","title":"Decorrelated Variable Importance","created":"2021-11-21","cited_by_count":13},{"id":"2007.08283","title":"Relative Feature Importance","created":"2020-07-16","cited_by_count":12},{"id":"2001.09055","title":"Forecasting Corn Yield with Machine Learning Ensembles","created":"2020-01-18","cited_by_count":10},{"id":"1904.03959","title":"Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations","created":"2019-04-08","cited_by_count":9},{"id":"2105.07033","title":"Cause and Effect: Hierarchical Concept-based Explanation of Neural Networks","created":"2021-05-14","cited_by_count":8},{"id":"2102.12328","title":"Bridging Breiman's Brook: From Algorithmic Modeling to Statistical Learning","created":"2021-02-23","cited_by_count":7},{"id":"2007.01062","title":"Are there any 'object detectors' in the hidden layers of CNNs trained to identify objects or scenes?","created":"2020-07-02","cited_by_count":6},{"id":"2111.15639","title":"DeDUCE: Generating Counterfactual Explanations Efficiently","created":"2021-11-29","cited_by_count":6},{"id":"2008.02915","title":"Kernel Ordinary Differential Equations","created":"2020-08-07","cited_by_count":5},{"id":"1704.04688","title":"Machine Learning and the Future of Realism","created":"2017-04-15","cited_by_count":4}],"next_cursor":null}