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Xilai Li et al · 1904
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“Three scenarios for continual learning”, 2019
Gido. van Ven and Andreas. Tolias · 1904
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“Improving and Understanding Variational Continual Learning”, 2019
Siddharth Swaroop, Cuong. Nguyen, Thang. Bui and Richard. Turner · 1905
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“Large Scale Incremental Learning”, 2019
Yue Wu et al · 1905
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Timothee Lesort et al · 1907
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“Online Continual Learning with Maximally Interfered Retrieval”, 2019
Rahaf Aljundi et al · 1908
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“Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset”, 2019
Bill Byrne et al · 1909
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“Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset”, 2020
Abhinav Rastogi et al · 1909
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“REMIND Your Neural Network to Prevent Catastrophic Forgetting”, 2020
Tyler. Hayes et al · 1910
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“A logical calculus of the ideas immanent in nervous activity”
Warren McCulloch and Walter Pitts · 1943
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“I.—COMPUTING MACHINERY AND INTELLIGENCE”
A.. TURING · 1950
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“On the identification problem”
L Zadeh · 1956
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“Reinforcement today.”
Burrhus Skinner · 1958
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“A variety op intelligent learning in a general problem solver”
A Newell and JC Shaw · 1959
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“Online Continual Learning from Imbalanced Data”
Aristotelis Chrysakis and Marie-Francine Moens · 1961
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“Numerical Identification of Linear Dynamic Systems from Normal Operating Records”
Karl Åström and Torsten Bohlin · 1965
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“Artificial intelligence progress report”, 1972
Marvin Minsky and Seymour Papert · 1972
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“An introduction to artificial intelligence: can computer think?”, 1978
Richard Bellman · 1978
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“Conceptual change in childhood”
Susan Carey · 1985
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“How many memory systems are there?”
Endel Tulving · 1985
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“Random sampling with a reservoir”
Jeffrey Vitter · 1985
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“Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-… hook”, 1987
Jürgen Schmidhuber · 1987
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“Conceptual and semantic development as theory change: The case of object permanence”
Alison Gopnik · 1988
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“Backpropagation applied to handwritten zip code recognition”
Yann LeCun et al · 1989
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“Catastrophic interference in connectionist networks: The sequential learning problem”
Michael McCloskey and Neal Cohen · 1989
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“A system for incremental learning based on algorithmic probability”
Ray Solomonoff · 1989
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“The age of intelligent machines”
Ray Kurzweil, Robert Richter, Ray Kurzweil and Martin Schneider · 1990
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“Connectionist models of recognition memory: constraints imposed by learning and forgetting functions.”
Roger Ratcliff · 1990
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“Principles of object perception”
Elizabeth Spelke · 1990
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“Artificial Intelligence: An Engineering Approach”
R Schalkoff · 1991
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“Principles of Risk Minimization for Learning Theory”
V. Vapnik · 1991
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“Theory of the backpropagation neural network”
Robert Hecht-Nielsen · 1992
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“A Practical Bayesian Framework for Backpropagation Networks”
David.. MacKay · 1992
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“Artificial Intelligence: Instructor’s Manual”
Elaine Rich and Kevin Knight · 1992
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“Uniqueness of the weights for minimal feedforward nets with a given input-output map”
Héctor Sussmann · 1992
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“Cognitive development: Foundational theories of core domains”
Henry Wellman and Susan Gelman · 1992
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“Artificial intelligence”
Patrick Winston · 1992
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“Learning and development in neural networks: The importance of starting small”
Jeffrey Elman · 1993
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“Design and evolution of modular neural network architectures”
Bart Happel and Jacob Murre · 1994
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“The Language Instinct. How the Mind Creates Language”, 1994
Steven Pinker · 1994
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“Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory.”
James McClelland, Bruce McNaughton and Randall O’Reilly · 1995
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“On combining artificial neural nets”
AMANDA SHARKEY · 1996
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“Explanation-based neural network learning”
Sebastian Thrun · 1996
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“Multitask learning”
Rich Caruana · 1997
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“Mind design II: philosophy, psychology, artificial intelligence”
John Haugeland · 1997
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“Flat minima”
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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“Long short-term memory”
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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“Modularity, combining and artificial neural nets”
AMANDA SHARKEY · 1997
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“Optimal robot excitation and identification”
Jan Swevers et al · 1997
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“Modular neural network classifiers: A comparative study”
Gasser Auda and Mohamed Kamel · 1998
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“Learning to Learn: Introduction and Overview”
Sebastian Thrun and Lorien Pratt · 1998
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“Modular neural networks: a survey”
Gasser Auda and Mohamed Kamel · 1999
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“Modularity in neural computing”
Terry Caelli, Ling Guan and Wilson Wen · 1999
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“Catastrophic forgetting in connectionist networks”
Robert French · 1999
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“Computing the physical parameters of rigid-body motion from video”
Kiran Bhat, Steven Seitz, Jovan Popović and Pradeep Khosla · 2002
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“Revealing modular organization in the yeast transcriptional network”
Jan Ihmels et al · 2002
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“Hippocampal and neocortical contributions to memory: Advances in the complementary learning systems framework”
Randall O’Reilly and Kenneth Norman · 2002
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“Adversarial Continual Learning”, 2020
Sayna Ebrahimi et al · 2003
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“Online Continual Learning on Sequences”
German. Parisi and Vincenzo Lomonaco · 2003
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“Using tf-idf to determine word relevance in document queries”
Juan Ramos · 2003
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“Online Convex Programming and Generalized Infinitesimal Gradient Ascent”
Martin Zinkevich · 2003
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“Defining Benchmarks for Continual Few-Shot Learning”, 2020
Antreas Antoniou, Massimiliano Patacchiola, Mateusz Ochal and Amos Storkey · 2004
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“A day of great illumination: BF Skinner’s discovery of shaping”
Gail Peterson · 2004
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“Online and batch learning of pseudo-metrics”
Shai Shalev-Shwartz, Yoram Singer and Andrew Ng · 2004
Earlier work this paper cites.
“Spontaneous evolution of modularity and network motifs”
Nadav Kashtan and Uri Alon · 2005
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“So how does the mind work?”
Steven Pinker · 2005
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“AI a modern approach”
Stuart Russell and Peter Norvig · 2005
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“Unifying Regularisation Methods for Continual Learning”, 2021
Frederik Benzing · 2006
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“Online multitask learning”
Ofer Dekel, Philip Long and Yoram Singer · 2006
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“System Identification without Lennart Ljung: what would have been different?”
Michel Gevers · 2006
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“Newell and Simon’s logic theorist: Historical background and impact on cognitive modeling”
Leo Gugerty · 2006
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“A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955”
John McCarthy, Marvin Minsky, Nathaniel Rochester and Claude Shannon · 2006
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“Understanding the Role of Training Regimes in Continual Learning”, 2020
Seyed Mirzadeh, Mehrdad Farajtabar, Razvan Pascanu and Hassan Ghasemzadeh · 2006
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“Modularity and community structure in networks”
Mark Newman · 2006
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“Variational free energy and the Laplace approximation”
Karl Friston et al · 2007
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“Variational Kullback-Leibler divergence for hidden Markov models”
John Hershey, Peder Olsen and Steven Rennie · 2007
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“Drinking from a Firehose: Continual Learning with Web-scale Natural Language”, 2020
Hexiang Hu, Ozan Sener, Fei Sha and Vladlen Koltun · 2007
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“Varying environments can speed up evolution”
Nadav Kashtan, Elad Noor and Uri Alon · 2007
Earlier work this paper cites.
“Online learning: Theory, algorithms, and applications”, 2007
Shai Shalev-shwartz and Prof Singer · 2007
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“Core knowledge”
Elizabeth Spelke and Katherine Kinzler · 2007
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“Sleep-dependent memory consolidation and reconsolidation”
Robert Stickgold and Matthew Walker · 2007
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“The road to modularity”
Günter Wagner, Mihaela Pavlicev and James Cheverud · 2007
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“Matrix regularization techniques for online multitask learning”
Alekh Agarwal, Alexander Rakhlin and Peter Bartlett · 2008
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“Transfer learning by distribution matching for targeted advertising”
Steffen Bickel, Christoph Sawade and Tobias Scheffer · 2008
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“Revealing modular architecture of human brain structural networks by using cortical thickness from MRI”
Zhang Chen et al · 2008
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“New Experiences Enhance Coordinated Neural Activity in the Hippocampus”
Sen Cheng and Loren Frank · 2008
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“Deep belief net learning in a long-range vision system for autonomous off-road driving”
Raia Hadsell et al · 2008
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“Online Metric Learning and Fast Similarity Search.”
Prateek Jain, Brian Kulis, Inderjit Dhillon and Kristen Grauman · 2008
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“Curriculum learning”
Yoshua Bengio, Jérôme Louradour, Ronan Collobert and Jason Weston · 2009
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“Complex brain networks: graph theoretical analysis of structural and functional systems”
Ed Bullmore and Olaf Sporns · 2009
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“Eigentransfer: a unified framework for transfer learning”
Wenyuan Dai et al · 2009
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“Imagenet: A large-scale hierarchical image database”
Jia Deng et al · 2009
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“Learning multiple layers of features from tiny images”
Alex Krizhevsky and Geoffrey Hinton · 2009
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“Perspectives on system identification”
Lennart Ljung · 2009
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“A survey on transfer learning”
Sinno Pan and Qiang Yang · 2009
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“A survey of manifold learning for images”
Robert Pless and Richard Souvenir · 2009
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“Herding dynamical weights to learn”
Max Welling · 2009
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“Induction, overhypotheses, and the shape bias: Some arguments and evidence for rational constructivism”
Fei Xu, Kathryn Dewar and Amy Perfors · 2009
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“Efficient physical embedding of topologically complex information processing networks in brains and computer circuits”
Danielle Bassett et al · 2010
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“Toward an architecture for never-ending language learning.”
Andrew Carlson et al · 2010
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“Hippocampal Replay Is Not a Simple Function of Experience”
Anoopum Gupta, Matthijs van Meer, David Touretzky and A Redish · 2010
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“MNIST handwritten digit database”
Yann LeCun, Corinna Cortes and CJ Burges · 2010
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“Modular and hierarchically modular organization of brain networks”
David Meunier, Renaud Lambiotte and Edward Bullmore · 2010
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“Continual Learning for Natural Language Generation in Task-oriented Dialog Systems”, 2020
Fei Mi et al · 2010
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“A literature survey on algorithms for multi-label learning”, 2010
Mohammad Sorower · 2010
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“Importance sampling: a review”
Surya Tokdar and Robert Kass · 2010
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“Transfer learning”
Lisa Torrey and Jude Shavlik · 2010
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“Notmnist dataset”, 2011
Yaroslav Bulatov · 2011
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“Reading digits in natural images with unsupervised feature learning”, 2011
Yuval Netzer et al · 2011
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“Double Updating Online Learning”
Peilin Zhao, Steven.. Hoi and Rong Jin · 2011
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“Deep learning of representations for unsupervised and transfer learning”
Yoshua Bengio · 2012
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“Matching cross-resolution face images using co-transfer learning”
Himanshu. Bhatt, Richa Singh, Mayank Vatsa and Nalini Ratha · 2012
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“Imagenet classification with deep convolutional neural networks”
Alex Krizhevsky, Ilya Sutskever and Geoff Hinton · 2012
Earlier work this paper cites.
“Continual Learning in Task-Oriented Dialogue Systems”, 2020
Andrea Madotto et al · 2012
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“Online Learning and Online Convex Optimization”
Shai Shalev-Shwartz · 2012
Earlier work this paper cites.
“Explanation-based neural network learning: A lifelong learning approach”
Sebastian Thrun · 2012
Earlier work this paper cites.
“Subspace identification for linear systems: Theory—Implementation—Applications”
Peter Van and BL De · 2012
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“On the optimization of a synaptic learning rule”
Samy Bengio, Yoshua Bengio, Jocelyn Cloutier and Jan Gescei · 2013
Earlier work this paper cites.
“Mitosis detection in breast cancer histology images with deep neural networks”
Dan Cireşan, Alessandro Giusti, Luca Gambardella and Jürgen Schmidhuber · 2013
Earlier work this paper cites.
“The evolutionary origins of modularity”
Jeff Clune, Jean-Baptiste Mouret and Hod Lipson · 2013
Earlier work this paper cites.
“OMS-TL: A Framework of Online Multiple Source Transfer Learning”
Liang Ge, Jing Gao and Aidong Zhang · 2013
Earlier work this paper cites.
“Evolving large-scale neural networks for vision-based reinforcement learning”
Jan Koutnik, Giuseppe Cuccu, Jurgen Schmidhuber and Faustino Gomez · 2013
Earlier work this paper cites.
“Collaborative online multitask learning”
Guangxia Li et al · 2013
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“The stability-plasticity dilemma: Investigating the continuum from catastrophic forgetting to age-limited learning effects”
Martial Mermillod, Aurélia Bugaiska and Patrick Bonin · 2013
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“Creating a false memory in the hippocampus”
Steve Ramirez et al · 2013
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“Recursive deep models for semantic compositionality over a sentiment treebank”
Richard Socher et al · 2013
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“Neural machine translation by jointly learning to align and translate”
Dzmitry Bahdanau, Kyunghyun Cho and Yoshua Bengio · 2014
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“Learning phrase representations using RNN encoder-decoder for statistical machine translation”
Kyunghyun Cho et al · 2014
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“Rich feature hierarchies for accurate object detection and semantic segmentation”
Ross Girshick, Jeff Donahue, Trevor Darrell and Jitendra Malik · 2014
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Alex Graves, Greg Wayne and Ivo Danihelka · 2014
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“Dopaminergic neurons promote hippocampal reactivation and spatial memory persistence”
Colin McNamara et al · 2014
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“John McCarthy dies at 84; the father of artificial intelligence”
Elaine Woo · 2014
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“Facial landmark detection by deep multi-task learning”
Zhanpeng Zhang, Ping Luo, Chen Loy and Xiaoou Tang · 2014
Earlier work this paper cites.
“Online Transfer Learning”
Peilin Zhao, Steven.H. Hoi, Jialei Wang and Bin Li · 2014
Earlier work this paper cites.
“Memory trace replay: the shaping of memory consolidation by neuromodulation.”
LA Atherton, D Dupret and JR Mellor · 2015
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“The modular and integrative functional architecture of the human brain”
Maxwell. Bertolero, B.. Yeo and Mark D’Esposito · 2015
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“Deepdriving: Learning affordance for direct perception in autonomous driving”
Chenyi Chen, Ari Seff, Alain Kornhauser and Jianxiong Xiao · 2015
Cited alongside, same era.
“Net2net: Accelerating learning via knowledge transfer”
Tianqi Chen, Ian Goodfellow and Jonathon Shlens · 2015
Cited alongside, same era.
“Lifelong Learning for Sentiment Classification”
Zhiyuan Chen, Nianzu Ma and Bing Liu · 2015
Cited alongside, same era.
“Binaryconnect: Training deep neural networks with binary weights during propagations”
Matthieu Courbariaux, Yoshua Bengio and Jean-Pierre David · 2015
Cited alongside, same era.
“An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks”, 2015
Ian. Goodfellow et al · 2015
Cited alongside, same era.
“Online Continual Learning with Maximal Interfered Retrieval”
Rahaf Aljundi et al · 2019
Later among the works it cites.
“Task-Free Continual Learning”
Rahaf Aljundi, Klaas Kelchtermans and Tinne Tuytelaars · 2019
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“Gradient based sample selection for online continual learning”
Rahaf Aljundi, Min Lin, Baptiste Goujaud and Yoshua Bengio · 2019
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“A review of modularization techniques in artificial neural networks”
Mohammed Amer and Tomás Maul · 2019
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“A meta-transfer objective for learning to disentangle causal mechanisms”
Yoshua Bengio et al · 2019
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“Automatically Composing Representation Transformations as a Means for Generalization”
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“Learning both weights and connections for efficient neural networks”
Song Han, Jeff Pool, John Tran and William Dally · 2015
Cited alongside, same era.
“Distilling the knowledge in a neural network”
Geoffrey Hinton, Oriol Vinyals and Jeff Dean · 2015
Cited alongside, same era.
“Siamese Neural Networks for One-Shot Image Recognition”, 2015
Gregory. Koch · 2015
Cited alongside, same era.
“Generative moment matching networks”
Yujia Li, Kevin Swersky and Rich Zemel · 2015
Cited alongside, same era.
“Human-level control through deep reinforcement learning”
Volodymyr Mnih et al · 2015
Cited alongside, same era.
“Hippocampal place cells construct reward related sequences through unexplored space”
H Ólafsdóttir et al · 2015
Cited alongside, same era.
“Curriculum learning of multiple tasks”
Anastasia Pentina, Viktoriia Sharmanska and Christoph Lampert · 2015
Cited alongside, same era.
Michael Chang, Abhishek Gupta, Sergey Levine and Thomas. Griffiths · 2019
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“Efficient Lifelong Learning with A-GEM”
Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach and Mohamed Elhoseiny · 2019
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“On tiny episodic memories in continual learning”
Arslan Chaudhry et al · 2019
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“BabyAI: First Steps Towards Grounded Language Learning With a Human In the Loop”
Maxime Chevalier-Boisvert et al · 2019
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“System identification: A machine learning perspective”
Alessandro Chiuso and Gianluigi Pillonetto · 2019
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“BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions”
Christopher Clark et al · 2019
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“Continual learning: A comparative study on how to defy forgetting in classification tasks”
Matthias De et al · 2019
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“Learning without memorizing”
Prithviraj Dhar et al · 2019
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“Uncertainty-guided continual learning with bayesian neural networks”
Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell and Marcus Rohrbach · 2019
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“Towards Robust Evaluations of Continual Learning”, 2019
Sebastian Farquhar and Yarin Gal · 2019
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“On The Power of Curriculum Learning in Training Deep Networks”
Guy Hacohen and Daphna Weinshall · 2019
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“Learning a Unified Classifier Incrementally via Rebalancing”
Saihui Hou et al · 2019
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“Parameter-efficient transfer learning for NLP”
Neil Houlsby et al · 2019
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“Meta-Learning Representations for Continual Learning”
Khurram Javed and Martha White · 2019
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“Large memory layers with product keys”
Guillaume Lample et al · 2019
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“Albert: A lite bert for self-supervised learning of language representations”
Zhenzhong Lan et al · 2019
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Mike Lewis et al · 2019
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“Compositional generalization for primitive substitutions”
Yuanpeng Li, Liang Zhao, Jianyu Wang and Joel Hestness · 2019
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“End-to-end multi-task learning with attention”
Shikun Liu, Edward Johns and Andrew Davison · 2019
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“Episodic Memory in Lifelong Language Learning”
Cyprien de Masson’Autume, Sebastian Ruder, Lingpeng Kong and Dani Yogatama · 2019
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“Learning to Adapt in Dynamic, Real-World Environments through Meta-Reinforcement Learning”
Anusha Nagabandi et al · 2019
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“Deep learning recommendation model for personalization and recommendation systems”
Maxim Naumov et al · 2019
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“Penn Discourse Treebank Version 3.0”
Rashmi Prasad, Bonnie Webber, Alan Lee and Aravind Joshi · 2019
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“Task-driven modular networks for zero-shot compositional learning”
Senthil Purushwalkam, Maximilian Nickel, Abhinav Gupta and Marc’Aurelio Ranzato · 2019
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“Language models are unsupervised multitask learners”
Alec Radford et al · 2019
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“Exploring the limits of transfer learning with a unified text-to-text transformer”
Colin Raffel et al · 2019
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“An adaptive random path selection approach for incremental learning”
Jathushan Rajasegaran et al · 2019
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“Random Path Selection for Continual Learning”
Jathushan Rajasegaran et al · 2019
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“Adaptive Posterior Learning: few-shot learning with a surprise-based memory module”
Tiago Ramalho and Marta Garnelo · 2019
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“Continual unsupervised representation learning”
Dushyant Rao et al · 2019
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“Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference”
Matthew Riemer et al · 2019
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“Recent advances and applications of machine learning in solid-state materials science”
Jonathan Schmidt, Mário Marques, Silvana Botti and Miguel Marques · 2019
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“wav2vec: Unsupervised pre-training for speech recognition”
Steffen Schneider, Alexei Baevski, Ronan Collobert and Michael Auli · 2019
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“Stanford researcher examines earliest concepts of artificial intelligence, robots in ancient myths”
Alex Shashkevich · 2019
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“Attending Over Triads for Learning Signed Network Embedding”
Shagun Sodhani, Meng Qu and Jian Tang · 2019
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“Bert and pals: Projected attention layers for efficient adaptation in multi-task learning”
Asa Stickland and Iain Murray · 2019
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“Incremental Object Learning From Contiguous Views”
Stefan Stojanov et al · 2019
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“Regularizing Deep Multi-Task Networks using Orthogonal Gradients”
Mihai Suteu and Yike Guo · 2019
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“A clinically applicable approach to continuous prediction of future acute kidney injury”
Nenad Tomašev et al · 2019
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“An Empirical Study of Example Forgetting during Deep Neural Network Learning”
Mariya Toneva et al · 2019
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“Generative replay with feedback connections as a general strategy for continual learning”, 2019
Gido. van Ven and Andreas. Tolias · 2019
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“Grandmaster level in StarCraft II using multi-agent reinforcement learning”
Oriol Vinyals et al · 2019
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“Neural network acceptability judgments”
Alex Warstadt, Amanpreet Singh and Samuel Bowman · 2019
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“Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems”
Chien-Sheng Wu et al · 2019
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“Agent57: Outperforming the atari human benchmark”
Adriàènech Badia et al · 2020
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“wav2vec 2.0: A framework for self-supervised learning of speech representations”
Alexei Baevski, Henry Zhou, Abdelrahman Mohamed and Michael Auli · 2020
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“Unveiling the predictive power of static structure in glassy systems”
Victor Bapst et al · 2020
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“Continual Lifelong Learning in Natural Language Processing: A Survey”
Magdalena Biesialska, Katarzyna Biesialska and Marta. Costa-jussà · 2020
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“Language models are few-shot learners”
Tom Brown et al · 2020
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Paweł Budzianowski et al · 2020
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“Orthogonal gradient descent for continual learning”
Mehrdad Farajtabar, Navid Azizan, Alex Mott and Ang Li · 2020
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“Online continual learning under extreme memory constraints”
Enrico Fini et al · 2020
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“Generative adversarial networks”
Ian Goodfellow et al · 2020
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“Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives”
Anirudh Goyal et al · 2020
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“Improved Schemes for Episodic Memory-based Lifelong Learning”
Yunhui Guo, Mingrui Liu, Tianbao Yang and Tajana Rosing · 2020
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“La-maml: Look-ahead meta learning for continual learning”
Gunshi Gupta, Karmesh Yadav and Liam Paull · 2020
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“Embracing Change: Continual Learning in Deep Neural Networks”
Raia Hadsell, Dushyant Rao, Andrei. Rusu and Razvan Pascanu · 2020
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“The Break-Even Point on Optimization Trajectories of Deep Neural Networks”
Stanislaw Jastrzebski et al · 2020
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“Continual learning with node-importance based adaptive group sparse regularization”
Sangwon Jung, Hongjoon Ahn, Sungmin Cha and Taesup Moon · 2020
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“Scaling laws for neural language models”
Jared Kaplan et al · 2020
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“Towards continual reinforcement learning: A review and perspectives”
Khimya Khetarpal, Matthew Riemer, Irina Rish and Doina Precup · 2020
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“Implicit discourse relation classification: We need to talk about evaluation”
Najoung Kim, Song Feng, Chulaka Gunasekara and Luis Lastras · 2020
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“Chaotic Continual Learning”, 2020
Touraj Laleh, Mojtaba Faramarzi, Irina Rish and Sarath Chandar · 2020
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“Compositional Language Continual Learning”
Yuanpeng Li, Liang Zhao, Kenneth Church and Mohamed Elhoseiny · 2020
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“Learning on the Job: Online Lifelong and Continual Learning”
Bing Liu · 2020
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