{"list":"other","total":51,"rows":[{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"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":"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":"1311.2901","title":"Visualizing and Understanding Convolutional Networks","created":"2013-11-12","cited_by_count":2290},{"id":"1704.02685","title":"Learning Important Features Through Propagating Activation Differences","created":"2017-04-10","cited_by_count":1458},{"id":"1912.02762","title":"Normalizing Flows for Probabilistic Modeling and Inference","created":"2019-12-05","cited_by_count":1323},{"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":"1806.07421","title":"RISE: Randomized Input Sampling for Explanation of Black-box Models","created":"2018-06-19","cited_by_count":838},{"id":"1705.07057","title":"Masked Autoregressive Flow for Density Estimation","created":"2017-05-19","cited_by_count":684},{"id":"1907.04809","title":"Variational Autoencoders and Nonlinear ICA: A Unifying Framework","created":"2019-07-10","cited_by_count":503},{"id":"1904.07451","title":"Counterfactual Visual Explanations","created":"2019-04-16","cited_by_count":313},{"id":"1711.09784","title":"Distilling a Neural Network Into a Soft Decision Tree","created":"2017-11-27","cited_by_count":236},{"id":"1907.02584","title":"Interpretable Counterfactual Explanations Guided by Prototypes","created":"2019-07-03","cited_by_count":223},{"id":"2006.06485","title":"Deep Structural Causal Models for Tractable Counterfactual Inference","created":"2020-06-11","cited_by_count":211},{"id":"2010.10596","title":"Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review","created":"2020-10-20","cited_by_count":174},{"id":"1910.13413","title":"Feature relevance quantification in explainable AI: A causal problem","created":"2019-10-29","cited_by_count":167},{"id":"2004.11165","title":"Multi-Objective Counterfactual Explanations","created":"2020-04-23","cited_by_count":157},{"id":"1906.04477","title":"Causal Discovery with Reinforcement Learning","created":"2019-06-11","cited_by_count":145},{"id":"1912.03277","title":"Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers","created":"2019-12-06","cited_by_count":144},{"id":"1907.07165","title":"Explaining Classifiers with Causal Concept Effect (CaCE)","created":"2019-07-16","cited_by_count":135},{"id":"2206.11104","title":"OpenXAI: Towards a Transparent Evaluation of Model Explanations","created":"2022-06-22","cited_by_count":126},{"id":"2209.03302","title":"Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures?","created":"2022-09-07","cited_by_count":125},{"id":"2011.01625","title":"Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models","created":"2020-11-03","cited_by_count":107},{"id":"1910.12336","title":"CXPlain: Causal Explanations for Model Interpretation under Uncertainty","created":"2019-10-27","cited_by_count":98},{"id":"2007.02863","title":"Counterfactual Data Augmentation using Locally Factored Dynamics","created":"2020-07-06","cited_by_count":93},{"id":"2006.01272","title":"Shapley explainability on the data manifold","created":"2020-06-01","cited_by_count":88},{"id":"1902.02302","title":"Neural Network Attributions: A Causal Perspective","created":"2019-02-06","cited_by_count":77},{"id":"2010.02637","title":"Weakly Supervised Disentangled Generative Causal Representation Learning","created":"2020-10-06","cited_by_count":68},{"id":"2011.04917","title":"Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End","created":"2020-11-10","cited_by_count":66},{"id":"2111.04138","title":"Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis","created":"2021-11-07","cited_by_count":65},{"id":"1707.01943","title":"A causal framework for explaining the predictions of black-box sequence-to-sequence models","created":"2017-07-06","cited_by_count":58},{"id":"2009.08270","title":"Evaluating and Mitigating Bias in Image Classifiers: A Causal Perspective Using Counterfactuals","created":"2020-09-17","cited_by_count":56},{"id":"2306.05415","title":"Causal normalizing flows: from theory to practice","created":"2023-06-08","cited_by_count":51},{"id":"2206.06821","title":"DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models","created":"2022-06-14","cited_by_count":41},{"id":"2205.07277","title":"Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations","created":"2022-05-15","cited_by_count":40},{"id":"2206.00801","title":"Indeterminacy in Generative Models: Characterization and Strong Identifiability","created":"2022-06-02","cited_by_count":33},{"id":"1902.02384","title":"Global Explanations of Neural Networks: Mapping the Landscape of Predictions","created":"2019-02-06","cited_by_count":32},{"id":"2106.00925","title":"Contrastive ACE: Domain Generalization Through Alignment of Causal Mechanisms","created":"2021-06-02","cited_by_count":20},{"id":"2401.00276","title":"Second-Order Uncertainty Quantification: Variance-Based Measures","created":"2023-12-30","cited_by_count":19},{"id":"1802.04253","title":"Global Model Interpretation via Recursive Partitioning","created":"2018-02-11","cited_by_count":17},{"id":"2112.15595","title":"Triangular Flows for Generative Modeling: Statistical Consistency, Smoothness Classes, and Fast Rates","created":"2021-12-31","cited_by_count":16},{"id":"2211.00472","title":"Backtracking Counterfactuals","created":"2022-11-01","cited_by_count":16},{"id":"2007.00714","title":"Quantifying intrinsic causal contributions via structure preserving interventions","created":"2020-07-01","cited_by_count":12},{"id":"2111.12490","title":"Matching Learned Causal Effects of Neural Networks with Domain Priors","created":"2021-11-24","cited_by_count":10},{"id":"2103.04613","title":"Fairness seen as Global Sensitivity Analysis","created":"2021-03-08","cited_by_count":9},{"id":"2206.06672","title":"Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows","created":"2022-06-14","cited_by_count":9},{"id":"2303.13850","title":"Towards Learning and Explaining Indirect Causal Effects in Neural Networks","created":"2023-03-24","cited_by_count":2},{"id":"2305.18183","title":"On Counterfactual Data Augmentation Under Confounding","created":"2023-05-29","cited_by_count":1},{"id":"2404.11208","title":"CAGE: Causality-Aware Shapley Value for Global Explanations","created":"2024-04-17","cited_by_count":1}],"next_cursor":null}