Fetching the paper…
Reading the bibliography…
Continual learning (CL) -- the ability to continuously learn, building on previously acquired knowledge -- is a natural requirement for long-lived autonomous reinforcement learning (RL) agents.
Random sampling with a reservoir
Jeffrey Scott Vitter · 1985
Earlier work this paper cites.
An introduction to the bootstrap
Bradley Efron and Robert J Tibshirani · 1994
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
Earlier work this paper cites.
Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
Earlier work this paper cites.
The arcade learning environment: An evaluation platform for general agents
M. G. Bellemare, Y. Naddaf, J. Veness, and M. Bowling · 2013
Earlier work this paper cites.
Weight uncertainty in neural network
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Lei Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 2016
Earlier work this paper cites.
Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil C. Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell · 2016
Earlier work this paper cites.
The forget-me-not process
Kieran Milan, Joel Veness, James Kirkpatrick, Michael H. Bowling, Anna Koop, and Demis Hassabis · 2016
Earlier work this paper cites.
Andrei A. Rusu, Neil C. Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
Earlier work this paper cites.
Neuroscience-inspired artificial intelligence
Demis Hassabis, Dharshan Kumaran, Christopher Summerfield, and Matthew Botvinick · 2017
Earlier work this paper cites.
Core50: a new dataset and benchmark for continuous object recognition
Vincenzo Lomonaco and Davide Maltoni · 2017
Earlier work this paper cites.
A deep hierarchical approach to lifelong learning in minecraft
Chen Tessler, Shahar Givony, Tom Zahavy, Daniel J. Mankowitz, and Shie Mannor · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Spinning Up in Deep Reinforcement Learning
Joshua Achiam · 2018
Earlier work this paper cites.
Memory aware synapses: Learning what (not) to forget
Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, and Tinne Tuytelaars · 2018
Earlier work this paper cites.
Adapting auxiliary losses using gradient similarity
Yunshu Du, Wojciech M. Czarnecki, Siddhant M. Jayakumar, Razvan Pascanu, and Balaji Lakshminarayanan · 2018
Earlier work this paper cites.
Towards robust evaluations of continual learning
Sebastian Farquhar and Yarin Gal · 2018
Cited alongside, same era.
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
Cited alongside, same era.
Soft actor-critic algorithms and applications
Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, and Sergey Levine · 2018
Cited alongside, same era.
Note on the quadratic penalties in elastic weight consolidation
Ferenc Huszár · 2018
Cited alongside, same era.
Packnet: Adding multiple tasks to a single network by iterative pruning
Arun Mallya and Svetlana Lazebnik · 2018
Cited alongside, same era.
Continual unsupervised representation learning
Dushyant Rao, Francesco Visin, Andrei A. Rusu, Razvan Pascanu, Yee Whye Teh, and Raia Hadsell · 2019
Later among the works it cites.
Ray interference: a source of plateaus in deep reinforcement learning
Tom Schaul, Diana Borsa, Joseph Modayil, and Razvan Pascanu · 2019
Later among the works it cites.
Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc V. Le · 2019
Later among the works it cites.
Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Chelsea Finn, and Sergey Levine · 2019
Later among the works it cites.
Continual learning with adaptive weights (CLAW)
Tameem Adel, Han Zhao, and Richard E. Turner · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Variational continual learning
Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, and Richard E. Turner · 2018
Cited alongside, same era.
Don’t forget, there is more than forgetting: new metrics for continual learning
Natalia Díaz Rodríguez, Vincenzo Lomonaco, David Filliat, and Davide Maltoni · 2018
Cited alongside, same era.
Towards a natural benchmark for continual learning
Jonathan Schwarz, Daniel Altman, Andrew Dudzik, Oriol Vinyals, Yee Whye Teh, and Razvan Pascanu · 2018
Cited alongside, same era.
Progress & compress: A scalable framework for continual learning
Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, and Raia Hadsell · 2018
Cited alongside, same era.
Online continual learning with maximal interfered retrieval
Rahaf Aljundi, Eugene Belilovsky, Tinne Tuytelaars, Laurent Charlin, Massimo Caccia, Min Lin, and Lucas Page-Caccia · 2019
Cited alongside, same era.
Large scale GAN training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
Cited alongside, same era.
Efficient lifelong learning with A-GEM
Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny · 2019
Cited alongside, same era.
On warm-starting neural network training
Jordan T. Ash and Ryan P. Adams · 2020
Later among the works it cites.
Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
Later among the works it cites.
Embracing change: Continual learning in deep neural networks
Raia Hadsell, Dushyant Rao, Andrei A. Rusu, and Razvan Pascanu · 2020
Later among the works it cites.
Towards continual reinforcement learning: A review and perspectives, 2020
Khimya Khetarpal, Matthew Riemer, Irina Rish, and Doina Precup · 2020
Later among the works it cites.
Continual reinforcement learning in 3d non-stationary environments
Vincenzo Lomonaco, Karan Desai, Eugenio Culurciello, and Davide Maltoni · 2020
Later among the works it cites.
Lifelong policy gradient learning of factored policies for faster training without forgetting
Jorge A. Mendez, Boyu Wang, and Eric Eaton · 2020
Later among the works it cites.
Jelly bean world: A testbed for never-ending learning
Emmanouil Antonios Platanios, Abulhair Saparov, and Tom M. Mitchell · 2020
Later among the works it cites.
Improved protein structure prediction using potentials from deep learning
Andrew W. Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Zídek, Alexander W. R. Nelson, Alex Bridgland, Hugo Penedones, Stig Petersen, Karen Simonyan, Steve Crossan, Pushmeet Kohli, David T. Jones, David Silver, Koray Kavukcuoglu, and Demis Hassabis · 2020
Later among the works it cites.
LAMOL: language modeling for lifelong language learning
Fan-Keng Sun, Cheng-Hao Ho, and Hung-Yi Lee · 2020
Later among the works it cites.
Causalworld: A robotic manipulation benchmark for causal structure and transfer learning
Ossama Ahmed, Frederik Träuble, Anirudh Goyal, Alexander Neitz, Manuel Wuthrich, Yoshua Bengio, Bernhard Schölkopf, and Stefan Bauer · 2021
Closest in time.
Evaluating online continual learning with calm, 2021
Germán Kruszewski, Ionut-Teodor Sorodoc, and Tomas Mikolov · 2021
Closest in time.
Continuous coordination as a realistic scenario for lifelong learning
Hadi Nekoei, Akilesh Badrinaaraayanan, Aaron C. Courville, and Sarath Chandar · 2021
Closest in time.
Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
Closest in time.