Fetching the paper…
Reading the bibliography…
Continual learning (CL) is a setting in which an agent has to learn from an incoming stream of data during its entire lifetime.
On angles between subspaces of a finite dimensional inner product space
Per Åke Wedin · 1983
Earlier work this paper cites.
Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J Cohen · 1989
Earlier work this paper cites.
A lifelong learning perspective for mobile robot control
Sebastian Thrun · 1995
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky et al · 2009
Earlier work this paper cites.
Machine lifelong learning: challenges and benefits for artificial general intelligence
Daniel L Silver · 2011
Earlier work this paper cites.
An empirical investigation of catastrophic forgetting in gradient-based neural networks
Ian J Goodfellow, Mehdi Mirza, Da Xiao, Aaron Courville, and Yoshua Bengio · 2013
Earlier work this paper cites.
Measuring catastrophic forgetting in neural networks
Ronald Kemker, Marc McClure, Angelina Abitino, Tyler Hayes, and Christopher Kanan · 2017
Earlier work this paper cites.
Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
Earlier work this paper cites.
Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
Earlier work this paper cites.
Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
Earlier work this paper cites.
Efficient lifelong learning with a-gem
Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny · 2018
Earlier work this paper cites.
Lifelong machine learning
Zhiyuan Chen and Bing Liu · 2018
Cited alongside, same era.
Gradient descent provably optimizes over-parameterized neural networks
Simon S Du, Xiyu Zhai, Barnabas Poczos, and Aarti Singh · 2018
Cited alongside, same era.
Towards robust evaluations of continual learning
Sebastian Farquhar and Yarin Gal · 2018
Cited alongside, same era.
Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 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.
Incremental learning through deep adaptation
Continual lifelong learning with neural networks: A review
German I Parisi, Ronald Kemker, Jose L Part, Christopher Kanan, and Stefan Wermter · 2019
Later among the works it cites.
Generalisation guarantees for continual learning with orthogonal gradient descent, 2020
Mehdi Abbana Bennani, Thang Doan, and Masashi Sugiyama · 2020
Closest in time.
Orthogonal gradient descent for continual learning
Mehrdad Farajtabar, Navid Azizan, Alex Mott, and Ang Li · 2020
Closest in time.
Understanding the role of training regimes in continual learning
Seyed Iman Mirzadeh, Mehrdad Farajtabar, Razvan Pascanu, and Hassan Ghasemzadeh · 2020
Closest in time.
Explaining how deep neural networks forget by deep visualization, 2020
Giang Nguyen, Shuan Chen, Tae Joon Jun, and Daeyoung Kim · 2020
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Amir Rosenfeld and John K Tsotsos · 2018
Cited alongside, same era.
An empirical study of example forgetting during deep neural network learning
Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, and Geoffrey J Gordon · 2018
Cited alongside, same era.
On exact computation with an infinitely wide neural net
Sanjeev Arora, Simon S Du, Wei Hu, Zhiyuan Li, Russ R Salakhutdinov, and Ruosong Wang · 2019
Cited alongside, same era.
Continual learning: A comparative study on how to defy forgetting in classification tasks
Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Ales Leonardis, Gregory Slabaugh, and Tinne Tuytelaars · 2019
Cited alongside, same era.
Wide neural networks of any depth evolve as linear models under gradient descent
Jaehoon Lee, Lechao Xiao, Samuel S Schoenholz, Yasaman Bahri, Roman Novak, Jascha Sohl-Dickstein, and Jeffrey Pennington · 2019
Cited alongside, same era.
Toward understanding catastrophic forgetting in continual learning
Cuong V Nguyen, Alessandro Achille, Michael Lam, Tal Hassner, Vijay Mahadevan, and Stefano Soatto · 2019
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
Cited in the paper.
Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard E Turner, and Mohammad Emtiyaz Khan · 2020
Closest in time.
Anatomy of catastrophic forgetting: Hidden representations and task semantics
Vinay V Ramasesh, Ethan Dyer, and Maithra Raghu · 2020
Closest in time.
Zeke Xie, Fengxiang He, Shaopeng Fu, Issei Sato, Dacheng Tao, and Masashi Sugiyama · 2020
Closest in time.
Sola: Continual learning with second-order loss approximation
Dong Yin, Mehrdad Farajtabar, and Ang Li · 2020
Closest in time.
Gradient surgery for multi-task learning
Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, and Chelsea Finn · 2020
Closest in time.
Angles between subspaces and their tangents
Peizhen Zhu and Andrew V Knyazev · 2020
Closest in time.