{"list":"other","total":57,"rows":[{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"1502.03167","title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift","created":"2015-02-11","cited_by_count":7005},{"id":"1905.11946","title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","created":"2019-05-28","cited_by_count":6442},{"id":"1607.06450","title":"Layer Normalization","created":"2016-07-21","cited_by_count":5669},{"id":"1612.00796","title":"Overcoming catastrophic forgetting in neural networks","created":"2016-12-02","cited_by_count":4787},{"id":"1611.07725","title":"iCaRL: Incremental Classifier and Representation Learning","created":"2016-11-23","cited_by_count":2333},{"id":"1605.08803","title":"Density estimation using Real NVP","created":"2016-05-27","cited_by_count":2042},{"id":"1706.08840","title":"Gradient Episodic Memory for Continual Learning","created":"2017-06-26","cited_by_count":1898},{"id":"1807.03039","title":"Glow: Generative Flow with Invertible 1x1 Convolutions","created":"2018-07-09","cited_by_count":1545},{"id":"1606.04671","title":"Progressive Neural Networks","created":"2016-06-15","cited_by_count":1361},{"id":"1802.07569","title":"Continual Lifelong Learning with Neural Networks: A Review","created":"2018-02-21","cited_by_count":1330},{"id":"1912.02762","title":"Normalizing Flows for Probabilistic Modeling and Inference","created":"2019-12-05","cited_by_count":1323},{"id":"1705.08690","title":"Continual Learning with Deep Generative Replay","created":"2017-05-24","cited_by_count":1158},{"id":"1812.00420","title":"Efficient Lifelong Learning with A-GEM","created":"2018-12-02","cited_by_count":1058},{"id":"2004.07211","title":"Dark Experience for General Continual Learning: a Strong, Simple Baseline","created":"2020-04-15","cited_by_count":911},{"id":"1606.05328","title":"Conditional Image Generation with PixelCNN Decoders","created":"2016-06-16","cited_by_count":862},{"id":"1708.01547","title":"Lifelong Learning with Dynamically Expandable Networks","created":"2017-08-04","cited_by_count":694},{"id":"1904.07734","title":"Three scenarios for continual learning","created":"2019-04-15","cited_by_count":614},{"id":"1810.01367","title":"FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models","created":"2018-10-02","cited_by_count":594},{"id":"1810.11910","title":"Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference","created":"2018-10-29","cited_by_count":521},{"id":"1406.5298","title":"Semi-Supervised Learning with Deep Generative Models","created":"2014-06-20","cited_by_count":496},{"id":"1810.09136","title":"Do Deep Generative Models Know What They Don't Know?","created":"2018-10-22","cited_by_count":443},{"id":"1910.07104","title":"Orthogonal Gradient Descent for Continual Learning","created":"2019-10-15","cited_by_count":369},{"id":"1811.00995","title":"Invertible Residual Networks","created":"2018-11-02","cited_by_count":365},{"id":"2103.09762","title":"Gradient Projection Memory for Continual Learning","created":"2021-03-17","cited_by_count":348},{"id":"1904.00310","title":"Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting","created":"2019-03-31","cited_by_count":302},{"id":"1505.00387","title":"Highway Networks","created":"2015-05-03","cited_by_count":260},{"id":"1906.02735","title":"Residual Flows for Invertible Generative Modeling","created":"2019-06-06","cited_by_count":257},{"id":"1810.12488","title":"Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines","created":"2018-10-30","cited_by_count":228},{"id":"2006.14769","title":"Supermasks in Superposition","created":"2020-06-26","cited_by_count":227},{"id":"1812.03596","title":"Task-Free Continual Learning","created":"2018-12-10","cited_by_count":221},{"id":"2006.08545","title":"Why Normalizing Flows Fail to Detect Out-of-Distribution Data","created":"2020-06-15","cited_by_count":221},{"id":"2006.06958","title":"Understanding the Role of Training Regimes in Continual Learning","created":"2020-06-12","cited_by_count":180},{"id":"2002.08165","title":"Using Hindsight to Anchor Past Knowledge in Continual Learning","created":"2020-02-19","cited_by_count":161},{"id":"1301.3584","title":"Revisiting Natural Gradient for Deep Networks","created":"2013-01-16","cited_by_count":159},{"id":"1910.14481","title":"Continual Unsupervised Representation Learning","created":"2019-10-31","cited_by_count":147},{"id":"2010.04495","title":"Linear Mode Connectivity in Multitask and Continual Learning","created":"2020-10-09","cited_by_count":145},{"id":"2001.00689","title":"A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning","created":"2020-01-03","cited_by_count":134},{"id":"1901.11356","title":"Functional Regularisation for Continual Learning with Gaussian Processes","created":"2019-01-31","cited_by_count":123},{"id":"1909.08383","title":"A continual learning survey: Defying forgetting in classification tasks","created":"2019-09-18","cited_by_count":118},{"id":"2003.12506","title":"Hybrid Models for Open Set Recognition","created":"2020-03-27","cited_by_count":109},{"id":"2004.14070","title":"Continual Deep Learning by Functional Regularisation of Memorable Past","created":"2020-04-29","cited_by_count":105},{"id":"1912.13025","title":"Semi-Supervised Learning with Normalizing Flows","created":"2019-12-30","cited_by_count":76},{"id":"2006.09273","title":"Density of States Estimation for Out-of-Distribution Detection","created":"2020-06-16","cited_by_count":74},{"id":"1803.10123","title":"Task Agnostic Continual Learning Using Online Variational Bayes","created":"2018-03-27","cited_by_count":61},{"id":"1906.05201","title":"Task Agnostic Continual Learning via Meta Learning","created":"2019-06-12","cited_by_count":53},{"id":"1808.06508","title":"Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies","created":"2018-08-20","cited_by_count":43},{"id":"1902.02767","title":"Hybrid Models with Deep and Invertible Features","created":"2019-02-07","cited_by_count":41},{"id":"2007.10221","title":"Learning latent representations across multiple data domains using Lifelong VAEGAN","created":"2020-07-20","cited_by_count":35},{"id":"1905.12019","title":"Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition","created":"2019-05-28","cited_by_count":26},{"id":"2010.02418","title":"The Effectiveness of Memory Replay in Large Scale Continual Learning","created":"2020-10-06","cited_by_count":26},{"id":"2004.11545","title":"Dropout as an Implicit Gating Mechanism For Continual Learning","created":"2020-04-24","cited_by_count":24},{"id":"1909.03742","title":"Efficient Continual Learning in Neural Networks with Embedding Regularization","created":"2019-09-09","cited_by_count":20},{"id":"1905.00505","title":"Semi-Conditional Normalizing Flows for Semi-Supervised Learning","created":"2019-05-01","cited_by_count":15},{"id":"2006.10974","title":"Optimization and Generalization of Regularization-Based Continual Learning: a Loss Approximation Viewpoint","created":"2020-06-19","cited_by_count":10},{"id":"2007.02443","title":"Pseudo-Rehearsal for Continual Learning with Normalizing Flows","created":"2020-07-05","cited_by_count":10},{"id":"1811.01146","title":"Closed-Loop Memory GAN for Continual Learning","created":"2018-11-03","cited_by_count":7}],"next_cursor":null}