Fetching the paper…
Reading the bibliography…
Despite the growing interest in continual learning, most of its contemporary works have been studied in a rather restricted setting where tasks are clearly distinguishable, and task boundaries are known during training.
On tiny episodic memories in continual learning
Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny, Thalaiyasingam Ajanthan, Puneet K. Dokania, Philip H. S. Torr, and Marc’Aurelio Ranzato · 1902
Earlier work this paper cites.
Mixtures of dirichlet processes with applications to bayesian nonparametric problems
Charles E. Antoniak · 1974
Earlier work this paper cites.
Bayesian density estimation by mixtures of normal distributions
Thomas S. Ferguson · 1983
Earlier work this paper cites.
Adaptive mixtures of local experts
Robert A. Jacobs, Michael I. Jordan, Steven J. Nowlan, and Geoffrey E. Hinton · 1991
Earlier work this paper cites.
Estimating normal means with a conjugate style dirichlet process prior
Steven Maceachern · 1994
Earlier work this paper cites.
Bayesian density estimation and inference using mixtures
Michael D. Escobar and Mike West · 1995
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Leon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Markov chain sampling methods for dirichlet process mixture models
Radford M. Neal · 2000
Earlier work this paper cites.
Infinite mixtures of gaussian process experts
Carl Edward Rasmussen and Zoubin Ghahramani · 2002
Earlier work this paper cites.
Variational inference for dirichlet process mixtures
David Blei and Michael Jordan · 2006
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A Krizhevsky and G Hinton · 2009
Earlier work this paper cites.
Nonlinear models using dirichlet process mixtures
Babak Shahbaba and Radford Neal · 2009
Earlier work this paper cites.
Dirichlet process
Yee Whye Teh · 2010
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
Earlier work this paper cites.
Fast bayesian inference in dirichlet process mixture models
Lianming Wang and David Dunson · 2011
Cited alongside, same era.
Online learning of nonparametric mixture models via sequential variational approximation
Dahua Lin · 2013
Cited alongside, same era.
Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2013
Cited alongside, same era.
Auto-Encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
Cited alongside, same era.
Importance weighted autoencoders
Yuri Burda, Roger Grosse, and Ruslan Salakhutdinov · 2015
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Outrageously large neural networks: The Sparsely-Gated Mixture-of-Experts layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean · 2017
Later among the works it cites.
Continual learning with deep generative replay
Hanul Shin, Jung Lee, Jaehong Kim, and Jiwon Kim · 2017
Later among the works it cites.
Memory aware synapses: Learning what (not) to forget
Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, and Tinne Tuytelaars · 2018
Later among the works it cites.
Re-evaluating continual learning scenarios: A categorization and case for strong baselines
Yen-Chang Hsu, Yen-Cheng Liu, Anita Ramasamy, and Zsolt Kira · 2018
Later among the works it cites.
Progress & compress: A scalable framework for continual learning
Jonathan Schwarz, Jelena Luketina, Wojciech Czarnecki, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, and Raia Hadsell · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Pixel recurrent neural networks
Aaron Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
Cited alongside, same era.
Progressive neural networks
Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
Cited alongside, same era.
Expert gate: Lifelong learning with a network of experts
Rahaf Aljundi, Punarjay Chakravarty, and Tinne Tuytelaars · 2017
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Cited alongside, same era.
Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell · 2017
Cited alongside, same era.
Learning without forgetting
Zhizhong Li and Derek Hoiem · 2017
Cited alongside, same era.
Gido M. van de Ven and Andreas S. Tolias · 2018
Later among the works it cites.
Reinforced continual learning
Ju Xu and Zhanxing Zhu · 2018
Later among the works it cites.
Lifelong learning with dynamically expandable networks
Jaehong Yoon, Eunho Yang, Jeongtae Lee, and Sung Ju Hwang · 2018
Later among the works it cites.
Reconciling meta-learning and continual learning with online mixtures of tasks
Ghassen Jerfel, Erin Grant, Thomas Griffiths, and Katherine Heller · 2019
Later among the works it cites.
Learn to grow: A continual structure learning framework for overcoming catastrophic forgetting
Xilai Li, Yingbo Zhou, Tianfu Wu, Richard Socher, and Caiming Xiong · 2019
Later among the works it cites.
Continuous learning in single-incremental-task scenarios
Davide Maltoni and Vincenzo Lomonaco · 2019
Later among the works it cites.
Deep online learning via Meta-Learning: continual adaptation for Model-Based RL
Anusha Nagabandi, Chelsea Finn, and Sergey Levine · 2019
Later among the works it cites.
Continual lifelong learning with neural networks: A review
German I. Parisi, Ronald Kemker, Jose L. Part, and Christopher Kanan · 2019
Later among the works it cites.
Continual unsupervised representation learning
Dushyant Rao, Francesco Visin, Andrei Rusu, Razvan Pascanu, Yee Whye Teh, and Raia Hadsell · 2019
Later among the works it cites.