{"list":"other","total":60,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"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":"1810.00826","title":"How Powerful are Graph Neural Networks?","created":"2018-10-01","cited_by_count":4393},{"id":"2005.00687","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","created":"2020-05-02","cited_by_count":2163},{"id":"1703.06103","title":"Modeling Relational Data with Graph Convolutional Networks","created":"2017-03-17","cited_by_count":1694},{"id":"1610.05492","title":"Federated Learning: Strategies for Improving Communication Efficiency","created":"2016-10-18","cited_by_count":1475},{"id":"1912.03817","title":"Machine Unlearning","created":"2019-12-09","cited_by_count":1061},{"id":"1903.03894","title":"GNNExplainer: Generating Explanations for Graph Neural Networks","created":"2019-03-10","cited_by_count":737},{"id":"1707.01476","title":"Convolutional 2D Knowledge Graph Embeddings","created":"2017-07-05","cited_by_count":730},{"id":"2205.12454","title":"Recipe for a General, Powerful, Scalable Graph Transformer","created":"2022-05-25","cited_by_count":713},{"id":"1911.04933","title":"Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks","created":"2019-11-12","cited_by_count":618},{"id":"1911.03030","title":"Certified Data Removal from Machine Learning Models","created":"2019-11-08","cited_by_count":535},{"id":"2203.02923","title":"GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation","created":"2022-03-06","cited_by_count":516},{"id":"2110.11309","title":"Fast Model Editing at Scale","created":"2021-10-21","cited_by_count":498},{"id":"1907.05012","title":"Making AI Forget You: Data Deletion in Machine Learning","created":"2019-07-11","cited_by_count":465},{"id":"1912.13213","title":"Online Learning: A Modern Introduction Using Convex Optimization","created":"2019-12-31","cited_by_count":417},{"id":"2103.03279","title":"Remember What You Want to Forget: Algorithms for Machine Unlearning","created":"2021-03-04","cited_by_count":371},{"id":"1708.02072","title":"Measuring Catastrophic Forgetting in Neural Networks","created":"2017-08-07","cited_by_count":360},{"id":"1707.03815","title":"Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking","created":"2017-07-12","cited_by_count":335},{"id":"2106.05234","title":"Do Transformers Really Perform Bad for Graph Representation?","created":"2021-06-09","cited_by_count":320},{"id":"2007.02923","title":"Descent-to-Delete: Gradient-Based Methods for Machine Unlearning","created":"2020-07-06","cited_by_count":311},{"id":"2109.13398","title":"Unrolling SGD: Understanding Factors Influencing Machine Unlearning","created":"2021-09-27","cited_by_count":279},{"id":"1908.11229","title":"White-box vs Black-box: Bayes Optimal Strategies for Membership Inference","created":"2019-08-29","cited_by_count":271},{"id":"2102.09548","title":"Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development","created":"2021-02-18","cited_by_count":264},{"id":"2002.10077","title":"Approximate Data Deletion from Machine Learning Models","created":"2020-02-24","cited_by_count":255},{"id":"2108.11577","title":"Machine Unlearning of Features and Labels","created":"2021-08-26","cited_by_count":229},{"id":"2111.08947","title":"Fast Yet Effective Machine Unlearning","created":"2021-11-17","cited_by_count":217},{"id":"2005.02205","title":"When Machine Unlearning Jeopardizes Privacy","created":"2020-05-05","cited_by_count":198},{"id":"2106.04378","title":"Adaptive Machine Unlearning","created":"2021-06-08","cited_by_count":176},{"id":"2110.11891","title":"On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning","created":"2021-10-22","cited_by_count":171},{"id":"2012.13431","title":"Mixed-Privacy Forgetting in Deep Networks","created":"2020-12-24","cited_by_count":163},{"id":"2006.14755","title":"DeltaGrad: Rapid retraining of machine learning models","created":"2020-06-26","cited_by_count":149},{"id":"2103.14991","title":"Graph Unlearning","created":"2021-03-27","cited_by_count":138},{"id":"2201.05629","title":"Zero-Shot Machine Unlearning","created":"2022-01-14","cited_by_count":133},{"id":"2009.05567","title":"Machine Unlearning for Random Forests","created":"2020-09-11","cited_by_count":131},{"id":"2102.13179","title":"Machine Unlearning via Algorithmic Stability","created":"2021-02-25","cited_by_count":130},{"id":"2203.07320","title":"The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining","created":"2022-03-14","cited_by_count":118},{"id":"2010.12883","title":"Variational Bayesian Unlearning","created":"2020-10-24","cited_by_count":117},{"id":"2110.11794","title":"Federated Unlearning via Class-Discriminative Pruning","created":"2021-10-22","cited_by_count":115},{"id":"2002.02730","title":"Machine Unlearning: Linear Filtration for Logit-based Classifiers","created":"2020-02-07","cited_by_count":95},{"id":"2201.06640","title":"Towards Adversarial Evaluations for Inexact Machine Unlearning","created":"2022-01-17","cited_by_count":84},{"id":"1812.07671","title":"Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RL","created":"2018-12-18","cited_by_count":81},{"id":"2101.06570","title":"Membership Inference Attack on Graph Neural Networks","created":"2021-01-17","cited_by_count":81},{"id":"2104.04883","title":"Graph Representation Learning in Biomedicine","created":"2021-04-11","cited_by_count":80},{"id":"2201.06820","title":"Recommendation Unlearning","created":"2022-01-18","cited_by_count":80},{"id":"2111.13654","title":"Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs","created":"2021-11-26","cited_by_count":79},{"id":"2201.09538","title":"Backdoor Defense with Machine Unlearning","created":"2022-01-24","cited_by_count":65},{"id":"2003.10933","title":"Learn to Forget: Machine Unlearning via Neuron Masking","created":"2020-03-24","cited_by_count":62},{"id":"2206.09140","title":"Certified Graph Unlearning","created":"2022-06-18","cited_by_count":46},{"id":"2106.15093","title":"Certifiable Machine Unlearning for Linear Models","created":"2021-06-29","cited_by_count":42},{"id":"2004.07229","title":"Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19","created":"2020-04-15","cited_by_count":39},{"id":"2012.15721","title":"Coded Machine Unlearning","created":"2020-12-31","cited_by_count":36},{"id":"2009.06419","title":"Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient Descent","created":"2020-09-11","cited_by_count":35},{"id":"2203.11491","title":"Making Recommender Systems Forget: Learning and Unlearning for Erasable Recommendation","created":"2022-03-22","cited_by_count":29},{"id":"2203.12964","title":"Knowledge Removal in Sampling-based Bayesian Inference","created":"2022-03-24","cited_by_count":21},{"id":"2105.06209","title":"DeepObliviate: A Powerful Charm for Erasing Data Residual Memory in Deep Neural Networks","created":"2021-05-13","cited_by_count":19},{"id":"2202.12232","title":"Bounding Membership Inference","created":"2022-02-24","cited_by_count":15},{"id":"2109.05922","title":"r-GAT: Relational Graph Attention Network for Multi-Relational Graphs","created":"2021-09-13","cited_by_count":9},{"id":"2111.05528","title":"Lightweight machine unlearning in neural network","created":"2021-11-10","cited_by_count":6}],"next_cursor":null}