{"list":"other","total":86,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"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":"1609.02907","title":"Semi-Supervised Classification with Graph Convolutional Networks","created":"2016-09-09","cited_by_count":11361},{"id":"1710.10903","title":"Graph Attention Networks","created":"2017-10-30","cited_by_count":8923},{"id":"1412.6572","title":"Explaining and Harnessing Adversarial Examples","created":"2014-12-20","cited_by_count":7364},{"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":"1411.4038","title":"Fully Convolutional Networks for Semantic Segmentation","created":"2014-11-14","cited_by_count":4859},{"id":"1409.0473","title":"Neural Machine Translation by Jointly Learning to Align and Translate","created":"2014-09-01","cited_by_count":4548},{"id":"1406.1078","title":"Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation","created":"2014-06-03","cited_by_count":4057},{"id":"1312.6114","title":"Auto-Encoding Variational Bayes","created":"2013-12-20","cited_by_count":4053},{"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":"2012.07436","title":"Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting","created":"2020-12-14","cited_by_count":2169},{"id":"1609.03499","title":"WaveNet: A Generative Model for Raw Audio","created":"2016-09-12","cited_by_count":1722},{"id":"1803.01271","title":"An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling","created":"2018-03-04","cited_by_count":1476},{"id":"1704.02685","title":"Learning Important Features Through Propagating Activation Differences","created":"2017-04-10","cited_by_count":1458},{"id":"1811.12359","title":"Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations","created":"2018-11-29","cited_by_count":941},{"id":"1902.10186","title":"Attention is not Explanation","created":"2019-02-26","cited_by_count":929},{"id":"1912.09363","title":"Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting","created":"2019-12-19","cited_by_count":761},{"id":"1908.04626","title":"Attention is not not Explanation","created":"2019-08-13","cited_by_count":636},{"id":"2005.11650","title":"Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks","created":"2020-05-24","cited_by_count":566},{"id":"1905.11481","title":"AI Feynman: a Physics-Inspired Method for Symbolic Regression","created":"2019-05-27","cited_by_count":548},{"id":"2202.07125","title":"Transformers in Time Series: A Survey","created":"2022-02-15","cited_by_count":497},{"id":"2012.15445","title":"Explainability in Graph Neural Networks: A Taxonomic Survey","created":"2020-12-31","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":"1611.06455","title":"Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline","created":"2016-11-20","cited_by_count":360},{"id":"1608.05745","title":"RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism","created":"2016-08-19","cited_by_count":349},{"id":"1910.13051","title":"ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels","created":"2019-10-29","cited_by_count":339},{"id":"1605.01713","title":"Not Just a Black Box: Learning Important Features Through Propagating Activation Differences","created":"2016-05-05","cited_by_count":297},{"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":"1808.00033","title":"Techniques for Interpretable Machine Learning","created":"2018-07-31","cited_by_count":238},{"id":"1612.08220","title":"Understanding Neural Networks through Representation Erasure","created":"2016-12-24","cited_by_count":224},{"id":"2002.00498","title":"DYNOTEARS: Structure Learning from Time-Series Data","created":"2020-02-02","cited_by_count":200},{"id":"2202.08516","title":"SAITS: Self-Attention-based Imputation for Time Series","created":"2022-02-17","cited_by_count":169},{"id":"1901.10912","title":"A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms","created":"2019-01-30","cited_by_count":158},{"id":"1802.00614","title":"Visual Interpretability for Deep Learning: a Survey","created":"2018-02-02","cited_by_count":155},{"id":"2202.01575","title":"CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting","created":"2022-02-03","cited_by_count":144},{"id":"1709.05206","title":"LSTM Fully Convolutional Networks for Time Series Classification","created":"2017-09-08","cited_by_count":136},{"id":"2209.11326","title":"Towards Faithful Model Explanation in NLP: A Survey","created":"2022-09-22","cited_by_count":130},{"id":"1906.05747","title":"Modeling the Dynamics of PDE Systems with Physics-Constrained Deep Auto-Regressive Networks","created":"2019-06-13","cited_by_count":129},{"id":"1904.02874","title":"An Attentive Survey of Attention Models","created":"2019-04-05","cited_by_count":124},{"id":"2206.15475","title":"Causal Machine Learning: A Survey and Open Problems","created":"2022-06-30","cited_by_count":110},{"id":"2010.13924","title":"Benchmarking Deep Learning Interpretability in Time Series Predictions","created":"2020-10-26","cited_by_count":102},{"id":"1911.12116","title":"Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey","created":"2019-11-27","cited_by_count":93},{"id":"2301.03028","title":"Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement","created":"2023-01-08","cited_by_count":90},{"id":"1706.07206","title":"Explaining Recurrent Neural Network Predictions in Sentiment Analysis","created":"2017-06-22","cited_by_count":88},{"id":"2003.03934","title":"Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation","created":"2020-03-09","cited_by_count":82},{"id":"1902.02302","title":"Neural Network Attributions: A Causal Perspective","created":"2019-02-06","cited_by_count":77},{"id":"2012.00073","title":"TimeSHAP: Explaining Recurrent Models through Sequence Perturbations","created":"2020-11-30","cited_by_count":74},{"id":"2104.00950","title":"Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey","created":"2021-04-02","cited_by_count":72},{"id":"1911.07757","title":"Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention","created":"2019-11-18","cited_by_count":71},{"id":"1909.07082","title":"Towards a Rigorous Evaluation of XAI Methods on Time Series","created":"2019-09-16","cited_by_count":67},{"id":"1710.11239","title":"Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics","created":"2017-10-30","cited_by_count":65},{"id":"2009.13211","title":"Instance-based Counterfactual Explanations for Time Series Classification","created":"2020-09-28","cited_by_count":64},{"id":"2105.02675","title":"Granger Causality: A Review and Recent Advances","created":"2021-05-05","cited_by_count":63},{"id":"2305.10032","title":"A Survey on Causal Discovery: Theory and Practice","created":"2023-05-17","cited_by_count":63},{"id":"2104.15135","title":"Explanation-Based Human Debugging of NLP Models: A Survey","created":"2021-04-30","cited_by_count":53},{"id":"2008.10781","title":"Counterfactual Explanations for Machine Learning on Multivariate Time Series Data","created":"2020-08-25","cited_by_count":49},{"id":"2101.07600","title":"Interpretable Models for Granger Causality Using Self-explaining Neural Networks","created":"2021-01-19","cited_by_count":45},{"id":"2202.07857","title":"Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series","created":"2022-02-16","cited_by_count":42},{"id":"1909.08118","title":"Physics-guided Convolutional Neural Network (PhyCNN) for Data-driven Seismic Response Modeling","created":"2019-09-17","cited_by_count":40},{"id":"1705.08498","title":"Clinical Intervention Prediction and Understanding using Deep Networks","created":"2017-05-23","cited_by_count":35},{"id":"2006.01667","title":"Interpretable Time Series Classification using Linear Models and Multi-resolution Multi-domain Symbolic Representations","created":"2020-05-31","cited_by_count":31},{"id":"2006.08849","title":"Preserving Dynamic Attention for Long-Term Spatial-Temporal Prediction","created":"2020-06-16","cited_by_count":30},{"id":"2201.13169","title":"Causal Explanations and XAI","created":"2022-01-31","cited_by_count":30},{"id":"1802.02952","title":"TSViz: Demystification of Deep Learning Models for Time-Series Analysis","created":"2018-02-08","cited_by_count":25},{"id":"1611.07634","title":"Interpretation of Prediction Models Using the Input Gradient","created":"2016-11-23","cited_by_count":24},{"id":"1904.12546","title":"ConvTimeNet: A Pre-trained Deep Convolutional Neural Network for Time Series Classification","created":"2019-04-29","cited_by_count":20},{"id":"1806.07385","title":"Detecting and interpreting myocardial infarction using fully convolutional neural networks","created":"2018-06-18","cited_by_count":19},{"id":"1908.07319","title":"Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks","created":"2019-08-20","cited_by_count":18},{"id":"2008.04882","title":"Spatiotemporal Attention for Multivariate Time Series Prediction and Interpretation","created":"2020-08-11","cited_by_count":18},{"id":"1806.08523","title":"Focusing on What is Relevant: Time-Series Learning and Understanding using Attention","created":"2018-06-22","cited_by_count":8},{"id":"2105.08179","title":"Learning Disentangled Representations for Time Series","created":"2021-05-17","cited_by_count":8},{"id":"2209.09114","title":"Neuro-symbolic Models for Interpretable Time Series Classification using Temporal Logic Description","created":"2022-09-15","cited_by_count":8},{"id":"1912.09855","title":"Explainability and Adversarial Robustness for RNNs","created":"2019-12-20","cited_by_count":7},{"id":"2004.12538","title":"Interpretation of Deep Temporal Representations by Selective Visualization of Internally Activated Nodes","created":"2020-04-27","cited_by_count":5},{"id":"2007.12617","title":"Attention Mechanism for Multivariate Time Series Recurrent Model Interpretability Applied to the Ironmaking Industry","created":"2020-07-15","cited_by_count":5},{"id":"2210.02176","title":"Feature Importance for Time Series Data: Improving KernelSHAP","created":"2022-10-05","cited_by_count":5},{"id":"2111.04942","title":"Learning from Multiple Time Series: A Deep Disentangled Approach to Diversified Time Series Forecasting","created":"2021-11-09","cited_by_count":4},{"id":"2203.07861","title":"Don't Get Me Wrong: How to Apply Deep Visual Interpretations to Time Series","created":"2022-03-14","cited_by_count":4},{"id":"1906.00917","title":"Learning Interpretable Shapelets for Time Series Classification through Adversarial Regularization","created":"2019-06-03","cited_by_count":3},{"id":"1811.08633","title":"Compensated Integrated Gradients to Reliably Interpret EEG Classification","created":"2018-11-21","cited_by_count":2}],"next_cursor":null}