{"list":"other","total":63,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"2010.11929","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","created":"2020-10-22","cited_by_count":30186},{"id":"2203.02155","title":"Training language models to follow instructions with human feedback","created":"2022-03-04","cited_by_count":15971},{"id":"2103.14030","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","created":"2021-03-25","cited_by_count":12139},{"id":"1412.6572","title":"Explaining and Harnessing Adversarial Examples","created":"2014-12-20","cited_by_count":7364},{"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":"1312.6199","title":"Intriguing properties of neural networks","created":"2013-12-21","cited_by_count":5155},{"id":"1602.04938","title":"\"Why Should I Trust You?\": Explaining the Predictions of Any Classifier","created":"2016-02-16","cited_by_count":5070},{"id":"1608.03983","title":"SGDR: Stochastic Gradient Descent with Warm Restarts","created":"2016-08-13","cited_by_count":5045},{"id":"1409.0473","title":"Neural Machine Translation by Jointly Learning to Align and Translate","created":"2014-09-01","cited_by_count":4548},{"id":"1502.01852","title":"Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification","created":"2015-02-06","cited_by_count":3611},{"id":"1912.04958","title":"Analyzing and Improving the Image Quality of StyleGAN","created":"2019-12-03","cited_by_count":3588},{"id":"1512.04150","title":"Learning Deep Features for Discriminative Localization","created":"2015-12-14","cited_by_count":2436},{"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":"1910.10045","title":"Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI","created":"2019-10-22","cited_by_count":1552},{"id":"1711.11279","title":"Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)","created":"2017-11-30","cited_by_count":1319},{"id":"1706.07269","title":"Explanation in Artificial Intelligence: Insights from the Social Sciences","created":"2017-06-22","cited_by_count":1206},{"id":"1810.03292","title":"Sanity Checks for Saliency Maps","created":"2018-10-08","cited_by_count":1156},{"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":"1710.11063","title":"Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks","created":"2017-10-30","cited_by_count":939},{"id":"2002.12327","title":"A Primer in BERTology: What we know about how BERT works","created":"2020-02-27","cited_by_count":815},{"id":"1610.08401","title":"Universal adversarial perturbations","created":"2016-10-26","cited_by_count":784},{"id":"1606.03490","title":"The Mythos of Model Interpretability","created":"2016-06-10","cited_by_count":721},{"id":"1907.10786","title":"Interpreting the Latent Space of GANs for Semantic Face Editing","created":"2019-07-25","cited_by_count":695},{"id":"1907.07374","title":"A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI","created":"2019-07-17","cited_by_count":404},{"id":"2101.05278","title":"GAN Inversion: A Survey","created":"2021-01-14","cited_by_count":367},{"id":"1611.01368","title":"Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies","created":"2016-11-04","cited_by_count":354},{"id":"1907.08610","title":"Lookahead Optimizer: k steps forward, 1 step back","created":"2019-07-19","cited_by_count":336},{"id":"2004.13912","title":"Neural Additive Models: Interpretable Machine Learning with Neural Nets","created":"2020-04-29","cited_by_count":319},{"id":"1704.03477","title":"A Neural Representation of Sketch Drawings","created":"2017-04-11","cited_by_count":313},{"id":"1907.02584","title":"Interpretable Counterfactual Explanations Guided by Prototypes","created":"2019-07-03","cited_by_count":223},{"id":"1804.11192","title":"Explainable Recommendation: A Survey and New Perspectives","created":"2018-04-30","cited_by_count":210},{"id":"1905.00780","title":"Full-Gradient Representation for Neural Network Visualization","created":"2019-05-02","cited_by_count":191},{"id":"2008.00299","title":"Eigen-CAM: Class Activation Map using Principal Components","created":"2020-08-01","cited_by_count":189},{"id":"2008.02312","title":"Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs","created":"2020-08-05","cited_by_count":151},{"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":"2004.11207","title":"Self-Attention Attribution: Interpreting Information Interactions Inside Transformer","created":"2020-04-23","cited_by_count":125},{"id":"2010.04050","title":"A survey of algorithmic recourse: definitions, formulations, solutions, and prospects","created":"2020-10-08","cited_by_count":118},{"id":"1902.02181","title":"Attention in Natural Language Processing","created":"2019-02-04","cited_by_count":101},{"id":"1805.10163","title":"Context-Aware Neural Machine Translation Learns Anaphora Resolution","created":"2018-05-25","cited_by_count":97},{"id":"1904.02632","title":"A Learned Representation for Scalable Vector Graphics","created":"2019-04-04","cited_by_count":91},{"id":"2002.10381","title":"Sketchformer: Transformer-based Representation for Sketched Structure","created":"2020-02-24","cited_by_count":78},{"id":"1804.08199","title":"Linguistically-Informed Self-Attention for Semantic Role Labeling","created":"2018-04-23","cited_by_count":71},{"id":"1703.09387","title":"Adversarial Transformation Networks: Learning to Generate Adversarial Examples","created":"2017-03-28","cited_by_count":62},{"id":"2104.06387","title":"ExplainaBoard: An Explainable Leaderboard for NLP","created":"2021-04-13","cited_by_count":50},{"id":"1805.00247","title":"Learning to Sketch with Shortcut Cycle Consistency","created":"2018-05-01","cited_by_count":47},{"id":"2004.00221","title":"NBDT: Neural-Backed Decision Trees","created":"2020-04-01","cited_by_count":46},{"id":"2011.10039","title":"Creative Sketch Generation","created":"2020-11-19","cited_by_count":44},{"id":"2007.02190","title":"B\\'ezierSketch: A generative model for scalable vector sketches","created":"2020-07-04","cited_by_count":43},{"id":"2005.09159","title":"Sketch-BERT: Learning Sketch Bidirectional Encoder Representation from Transformers by Self-supervised Learning of Sketch Gestalt","created":"2020-05-19","cited_by_count":42},{"id":"1804.04804","title":"Learning Deep Sketch Abstraction","created":"2018-04-13","cited_by_count":36},{"id":"1804.01401","title":"SketchMate: Deep Hashing for Million-Scale Human Sketch Retrieval","created":"2018-04-04","cited_by_count":35},{"id":"1805.04218","title":"Deep RNNs Encode Soft Hierarchical Syntax","created":"2018-05-11","cited_by_count":34},{"id":"1902.02384","title":"Global Explanations of Neural Networks: Mapping the Landscape of Predictions","created":"2019-02-06","cited_by_count":32},{"id":"2012.10076","title":"Semantics and explanation: why counterfactual explanations produce adversarial examples in deep neural networks","created":"2020-12-18","cited_by_count":24},{"id":"2009.04806","title":"SketchEmbedNet: Learning Novel Concepts by Imitating Drawings","created":"2020-08-27","cited_by_count":18},{"id":"1709.05321","title":"Learning Functional Causal Models with Generative Neural Networks","created":"2017-09-15","cited_by_count":14},{"id":"1808.02312","title":"Universal Perceptual Grouping","created":"2018-08-07","cited_by_count":14},{"id":"2207.13543","title":"Abstracting Sketches through Simple Primitives","created":"2022-07-27","cited_by_count":14},{"id":"1811.08170","title":"Sketch-R2CNN: An Attentive Network for Vector Sketch Recognition","created":"2018-11-20","cited_by_count":6}],"next_cursor":null}