{"list":"other","total":123,"rows":[{"id":"1312.6114","title":"Auto-Encoding Variational Bayes","created":"2013-12-20","cited_by_count":4053},{"id":"1312.5602","title":"Playing Atari with Deep Reinforcement Learning","created":"2013-12-19","cited_by_count":3274},{"id":"1802.03426","title":"UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction","created":"2018-02-09","cited_by_count":2898},{"id":"1612.06890","title":"CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning","created":"2016-12-20","cited_by_count":1457},{"id":"1506.02025","title":"Spatial Transformer Networks","created":"2015-06-05","cited_by_count":1429},{"id":"1609.04836","title":"On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima","created":"2016-09-15","cited_by_count":1352},{"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":"1712.09913","title":"Visualizing the Loss Landscape of Neural Nets","created":"2017-12-28","cited_by_count":1130},{"id":"2007.04612","title":"Concept Bottleneck Models","created":"2020-07-09","cited_by_count":1032},{"id":"2006.15055","title":"Object-Centric Learning with Slot Attention","created":"2020-06-26","cited_by_count":876},{"id":"1606.03657","title":"InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets","created":"2016-06-12","cited_by_count":860},{"id":"1806.10574","title":"This Looks Like That: Deep Learning for Interpretable Image Recognition","created":"2018-06-27","cited_by_count":737},{"id":"2002.00444","title":"Deep Reinforcement Learning for Autonomous Driving: A Survey","created":"2020-02-02","cited_by_count":721},{"id":"1710.09829","title":"Dynamic Routing Between Capsules","created":"2017-10-26","cited_by_count":689},{"id":"1802.05983","title":"Disentangling by Factorising","created":"2018-02-16","cited_by_count":616},{"id":"2011.03395","title":"Underspecification Presents Challenges for Credibility in Modern Machine Learning","created":"2020-11-06","cited_by_count":496},{"id":"1703.04933","title":"Sharp Minima Can Generalize For Deep Nets","created":"2017-03-15","cited_by_count":466},{"id":"1807.00431","title":"Confounding variables can degrade generalization performance of radiological deep learning models","created":"2018-07-02","cited_by_count":379},{"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":"1902.10178","title":"Unmasking Clever Hans Predictors and Assessing What Machines Really Learn","created":"2019-02-26","cited_by_count":365},{"id":"1611.01838","title":"Entropy-SGD: Biasing Gradient Descent Into Wide Valleys","created":"2016-11-06","cited_by_count":359},{"id":"2004.13912","title":"Neural Additive Models: Interpretable Machine Learning with Neural Nets","created":"2020-04-29","cited_by_count":319},{"id":"1908.08796","title":"Reinforcement Learning in Healthcare: A Survey","created":"2019-08-22","cited_by_count":310},{"id":"1812.02230","title":"Towards a Definition of Disentangled Representations","created":"2018-12-05","cited_by_count":296},{"id":"1606.01541","title":"Deep Reinforcement Learning for Dialogue Generation","created":"2016-06-05","cited_by_count":288},{"id":"1901.06314","title":"Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data","created":"2019-01-18","cited_by_count":262},{"id":"1802.07810","title":"Manipulating and Measuring Model Interpretability","created":"2018-02-21","cited_by_count":256},{"id":"1909.09223","title":"InterpretML: A Unified Framework for Machine Learning Interpretability","created":"2019-09-19","cited_by_count":246},{"id":"1801.06637","title":"Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations","created":"2018-01-20","cited_by_count":235},{"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":"1704.02532","title":"Deep Reinforcement Learning framework for Autonomous Driving","created":"2017-04-08","cited_by_count":225},{"id":"2002.01650","title":"Concept Whitening for Interpretable Image Recognition","created":"2020-02-05","cited_by_count":225},{"id":"2012.02046","title":"Neural Prototype Trees for Interpretable Fine-grained Image Recognition","created":"2020-12-03","cited_by_count":214},{"id":"1909.06342","title":"Explainable Machine Learning in Deployment","created":"2019-09-13","cited_by_count":211},{"id":"1803.04765","title":"Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning","created":"2018-03-13","cited_by_count":207},{"id":"1804.02477","title":"Programmatically Interpretable Reinforcement Learning","created":"2018-04-06","cited_by_count":193},{"id":"1412.6856","title":"Object Detectors Emerge in Deep Scene CNNs","created":"2014-12-22","cited_by_count":179},{"id":"1805.08328","title":"Verifiable Reinforcement Learning via Policy Extraction","created":"2018-05-22","cited_by_count":177},{"id":"2012.04456","title":"Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization","created":"2020-12-08","cited_by_count":173},{"id":"1802.04942","title":"Isolating Sources of Disentanglement in Variational Autoencoders","created":"2018-02-14","cited_by_count":167},{"id":"1909.06677","title":"Predictive Multiplicity in Classification","created":"2019-09-14","cited_by_count":166},{"id":"2004.07213","title":"Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims","created":"2020-04-15","cited_by_count":165},{"id":"1710.00935","title":"Interpretable Convolutional Neural Networks","created":"2017-10-02","cited_by_count":162},{"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":"1511.01644","title":"Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model","created":"2015-11-05","cited_by_count":140},{"id":"2001.04536","title":"Understanding and mitigating gradient pathologies in physics-informed neural networks","created":"2020-01-13","cited_by_count":135},{"id":"1810.00825","title":"Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks","created":"2018-10-01","cited_by_count":132},{"id":"2102.12627","title":"How to represent part-whole hierarchies in a neural network","created":"2021-02-25","cited_by_count":123},{"id":"1503.03167","title":"Deep Convolutional Inverse Graphics Network","created":"2015-03-11","cited_by_count":121},{"id":"1909.10145","title":"PPINN: Parareal Physics-Informed Neural Network for time-dependent PDEs","created":"2019-09-23","cited_by_count":118},{"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":"1904.12847","title":"Optimal Sparse Decision Trees","created":"2019-04-29","cited_by_count":105},{"id":"1711.05611","title":"Interpreting Deep Visual Representations via Network Dissection","created":"2017-11-15","cited_by_count":103},{"id":"2006.08690","title":"Generalized and Scalable Optimal Sparse Decision Trees","created":"2020-06-15","cited_by_count":100},{"id":"1907.09294","title":"The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations","created":"2019-07-22","cited_by_count":97},{"id":"2006.06848","title":"Getting a CLUE: A Method for Explaining Uncertainty Estimates","created":"2020-06-11","cited_by_count":92},{"id":"1801.01236","title":"Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems","created":"2018-01-04","cited_by_count":89},{"id":"1906.02500","title":"Towards Interpretable Reinforcement Learning Using Attention Augmented Agents","created":"2019-06-06","cited_by_count":82},{"id":"1906.06818","title":"Stacked Capsule Autoencoders","created":"2019-06-17","cited_by_count":82},{"id":"1805.09901","title":"Boolean Decision Rules via Column Generation","created":"2018-05-24","cited_by_count":80},{"id":"2006.08472","title":"Physics informed deep learning for computational elastodynamics without labeled data","created":"2020-06-10","cited_by_count":80},{"id":"1602.00370","title":"Visualizing Large-scale and High-dimensional Data","created":"2016-02-01","cited_by_count":79},{"id":"1712.07294","title":"Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning","created":"2017-12-20","cited_by_count":77},{"id":"1502.04269","title":"Supersparse Linear Integer Models for Optimized Medical Scoring Systems","created":"2015-02-15","cited_by_count":76},{"id":"1503.01161","title":"The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification","created":"2015-03-03","cited_by_count":76},{"id":"1712.09005","title":"Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding","created":"2017-12-25","cited_by_count":75},{"id":"1806.01830","title":"Relational Deep Reinforcement Learning","created":"2018-06-05","cited_by_count":73},{"id":"1806.06988","title":"Deep Neural Decision Trees","created":"2018-06-19","cited_by_count":72},{"id":"1907.09728","title":"Interpretable and Steerable Sequence Learning via Prototypes","created":"2019-07-23","cited_by_count":72},{"id":"1602.02658","title":"Graying the black box: Understanding DQNs","created":"2016-02-08","cited_by_count":68},{"id":"1807.05887","title":"Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees","created":"2018-07-16","cited_by_count":65},{"id":"2101.00352","title":"Characterizing Fairness Over the Set of Good Models Under Selective Labels","created":"2021-01-02","cited_by_count":65},{"id":"1602.08610","title":"Scalable Bayesian Rule Lists","created":"2016-02-27","cited_by_count":64},{"id":"2007.12652","title":"MurTree: Optimal Classification Trees via Dynamic Programming and Search","created":"2020-07-24","cited_by_count":63},{"id":"1912.02523","title":"Towards Explainable Deep Neural Networks (xDNN)","created":"2019-12-05","cited_by_count":61},{"id":"1910.00204","title":"TriMap: Large-scale Dimensionality Reduction Using Triplets","created":"2019-10-01","cited_by_count":60},{"id":"1912.05743","title":"Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning","created":"2019-12-09","cited_by_count":60},{"id":"1411.5899","title":"Falling Rule Lists","created":"2014-11-21","cited_by_count":59},{"id":"1908.09961","title":"Theory and Evaluation Metrics for Learning Disentangled Representations","created":"2019-08-26","cited_by_count":57},{"id":"1811.00731","title":"The age of secrecy and unfairness in recidivism prediction","created":"2018-11-02","cited_by_count":53},{"id":"1704.01701","title":"Learning Certifiably Optimal Rule Lists for Categorical Data","created":"2017-04-06","cited_by_count":50},{"id":"1811.03695","title":"Deep Learning Predicts Hip Fracture using Confounding Patient and Healthcare Variables","created":"2018-11-08","cited_by_count":50},{"id":"2011.02863","title":"This Looks Like That, Because ... Explaining Prototypes for Interpretable Image Recognition","created":"2020-11-05","cited_by_count":50},{"id":"1907.10882","title":"Interpretability Beyond Classification Output: Semantic Bottleneck Networks","created":"2019-07-25","cited_by_count":49},{"id":"1610.00168","title":"Learning Optimized Risk Scores","created":"2016-10-01","cited_by_count":47},{"id":"2006.14804","title":"Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation","created":"2020-06-26","cited_by_count":42},{"id":"2003.02428","title":"ViCE: Visual Counterfactual Explanations for Machine Learning Models","created":"2020-03-05","cited_by_count":34},{"id":"1811.12615","title":"An Interpretable Model with Globally Consistent Explanations for Credit Risk","created":"2018-11-30","cited_by_count":33},{"id":"1907.01180","title":"Conservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree Policy","created":"2019-07-02","cited_by_count":29},{"id":"1902.02950","title":"Differentiable Physics-informed Graph Networks","created":"2019-02-08","cited_by_count":25},{"id":"1801.09271","title":"Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Data","created":"2018-01-28","cited_by_count":23},{"id":"1806.11187","title":"Neural-net-induced Gaussian process regression for function approximation and PDE solution","created":"2018-06-22","cited_by_count":22},{"id":"1912.00512","title":"Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning","created":"2019-12-01","cited_by_count":19},{"id":"1901.04878","title":"Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems","created":"2019-01-15","cited_by_count":18},{"id":"1707.06315","title":"FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference","created":"2017-07-19","cited_by_count":16},{"id":"1903.11420","title":"Do Not Trust Additive Explanations","created":"2019-03-27","cited_by_count":16},{"id":"2007.15140","title":"Computing Optimal Decision Sets with SAT","created":"2020-07-29","cited_by_count":16},{"id":"1511.02210","title":"Learning Optimized Or's of And's","created":"2015-11-06","cited_by_count":14},{"id":"1812.01843","title":"MLIC: A MaxSAT-Based framework for learning interpretable classification rules","created":"2018-12-05","cited_by_count":14}],"next_cursor":"100"}