Fetching the paper…
Reading the bibliography…
The ability to continuously learn and adapt itself to new tasks, without losing grasp of already acquired knowledge is a hallmark of biological learning systems, which current deep learning systems fall short of.
Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J Cohen · 1989
Earlier work this paper cites.
Autoassociative neural networks
Mark A Kramer · 1992
Earlier work this paper cites.
Memory consolidation and the medial temporal lobe: a simple network model
Pablo Alvarez and Larry R Squire · 1994
Earlier work this paper cites.
Reactivation of hippocampal ensemble memories during sleep
Matthew A Wilson and Bruce L McNaughton · 1994
Earlier work this paper cites.
Consolidation in neural networks and in the sleeping brain
Anthony Robins · 1996
Earlier work this paper cites.
Domain-specific knowledge systems in the brain: The animate-inanimate distinction
Alfonso Caramazza and Jennifer R Shelton · 1998
Earlier work this paper cites.
The mnist database of handwritten digits
Yann LeCun · 1998
Earlier work this paper cites.
Catastrophic forgetting in connectionist networks
Robert M French · 1999
Earlier work this paper cites.
The organization of conceptual knowledge: the evidence from category-specific semantic deficits
Alfonso Caramazza and Bradford Z Mahon · 2003
Earlier work this paper cites.
The psychology and neuroscience of forgetting
John T Wixted · 2004
Earlier work this paper cites.
Reconsolidation of episodic memories: A subtle reminder triggers integration of new information
Almut Hupbach, Rebecca Gomez, Oliver Hardt, and Lynn Nadel · 2007
Earlier work this paper cites.
Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Category-specific organization in the human brain does not require visual experience
Bradford Z Mahon, Stefano Anzellotti, Jens Schwarzbach, Massimiliano Zampini, and Alfonso Caramazza · 2009
Earlier work this paper cites.
Sleep spindle activity is associated with the integration of new memories and existing knowledge
Jakke Tamminen, Jessica D Payne, Robert Stickgold, Erin J Wamsley, and M Gareth Gaskell · 2010
Earlier work this paper cites.
Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Semi-supervised learning with deep generative models
Durk P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning · 2014
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
Earlier work this paper cites.
Memory consolidation
Larry R Squire, Lisa Genzel, John T Wixted, and Richard G Morris · 2015
Earlier work this paper cites.
David Ha, Andrew Dai, and Quoc V Le · 2016
Earlier work this paper cites.
How concepts are encoded in the human brain: a modality independent, category-based cortical organization of semantic knowledge
Giacomo Handjaras, Emiliano Ricciardi, Andrea Leo, Alessandro Lenci, Luca Cecchetti, Mirco Cosottini, Giovanna Marotta, and Pietro Pietrini · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 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.
Meta-learning with memory-augmented neural networks
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lillicrap · 2016
Earlier work this paper cites.
Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al · 2016
Earlier work this paper cites.
Smash: one-shot model architecture search through hypernetworks
Andrew Brock, Theodore Lim, James M Ritchie, and Nick Weston · 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 A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
Cited alongside, same era.
Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
Cited alongside, same era.
A simple neural attentive meta-learner
Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel · 2017
Cited alongside, same era.
Gradient based sample selection for online continual learning
Rahaf Aljundi, Min Lin, Baptiste Goujaud, and Yoshua Bengio · 2019
Later among the works it cites.
Online learned continual compression with adaptative quantization module
Lucas Caccia, Eugene Belilovsky, Massimo Caccia, and Joelle Pineau · 2019
Later among the works it cites.
Efficient lifelong learning with a-gem
Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny · 2019
Later among the works it cites.
Continual learning with tiny episodic memories
Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny, Thalaiyasingam Ajanthan, Puneet K Dokania, Philip HS Torr, and Marc’Aurelio Ranzato · 2019
Later among the works it cites.
Facilitating bayesian continual learning by natural gradients and stein gradients
Yu Chen, Tom Diethe, and Neil Lawrence · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cuong V Nguyen, Yingzhen Li, Thang D Bui, and Richard E Turner · 2017
Cited alongside, same era.
Automatic differentiation in PyTorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Cited alongside, same era.
icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert · 2017
Cited alongside, same era.
Routing networks: Adaptive selection of non-linear functions for multi-task learning
Clemens Rosenbaum, Tim Klinger, and Matthew Riemer · 2017
Cited alongside, same era.
Continual learning with deep generative replay
Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
Cited alongside, same era.
Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
Cited alongside, same era.
Later among the works it cites.
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
Later among the works it cites.
Orthogonal gradient descent for continual learning, 2019
Mehrdad Farajtabar, Navid Azizan, Alex Mott, and Ang Li · 2019
Later among the works it cites.
Online meta-learning
Chelsea Finn, Aravind Rajeswaran, Sham Kakade, and Sergey Levine · 2019
Later among the works it cites.
Can sleep protect memories from catastrophic forgetting?
Oscar C Gonzalez, Yury Sokolov, Giri Krishnan, and Maxim Bazhenov · 2019
Later among the works it cites.
Remind your neural network to prevent catastrophic forgetting
Tyler L Hayes, Kushal Kafle, Robik Shrestha, Manoj Acharya, and Christopher Kanan · 2019
Later among the works it cites.
Meta-learning representations for continual learning
Khurram Javed and Martha White · 2019
Later among the works it cites.
Sleep-dependent selective imitation in infants
Carolin Konrad, Nora D Dirks, Annegret Warmuth, Jane S Herbert, Silvia Schneider, and Sabine Seehagen · 2019
Later among the works it cites.
Generative models from the perspective of continual learning
Timothée Lesort, Hugo Caselles-Dupré, Michael Garcia-Ortiz, Jean-François Goudou, and David Filliat · 2019
Later among the works it cites.
Marginal replay vs conditional replay for continual learning
Timothée Lesort, Alexander Gepperth, Andrei Stoian, and David Filliat · 2019
Later among the works it cites.
Zero-shot task transfer
Arghya Pal and Vineeth N Balasubramanian · 2019
Later among the works it cites.
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.
Random path selection for incremental learning
Jathushan Rajasegaran, Munawar Hayat, Salman H. Khan, Fahad Shahbaz Khan, and Ling Shao · 2019
Later among the works it cites.
Experience replay for continual learning
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lillicrap, and Gregory Wayne · 2019
Later among the works it cites.
Functional regularisation for continual learning with gaussian processes
Michalis K Titsias, Jonathan Schwarz, Alexander G de G Matthews, Razvan Pascanu, and Yee Whye Teh · 2019
Later among the works it cites.
Three scenarios for continual learning
Gido M van de Ven and Andreas S Tolias · 2019
Later among the works it cites.
A meta-learning framework for generalized zero-shot learning
Vinay Kumar Verma, Dhanajit Brahma, and Piyush Rai · 2019
Later among the works it cites.
Continual learning with hypernetworks
Johannes von Oswald, Christian Henning, João Sacramento, and Benjamin F Grewe · 2019
Later among the works it cites.
Oracle: Order robust adaptive continual learning
Jaehong Yoon, Saehoon Kim, Eunho Yang, and Sung Ju Hwang · 2019
Later among the works it cites.
Side-tuning: Network adaptation via additive side networks
Jeffrey O Zhang, Alexander Sax, Amir Zamir, Leonidas Guibas, and Jitendra Malik · 2019
Later among the works it cites.
Fast context adaptation via meta-learning
Luisa Zintgraf, Kyriacos Shiarli, Vitaly Kurin, Katja Hofmann, and Shimon Whiteson · 2019
Later among the works it cites.
Shawn Beaulieu, Lapo Frati, Thomas Miconi, Joel Lehman, Kenneth O Stanley, Jeff Clune, and Nick Cheney · 2020
Closest in time.
Uncertainty-guided continual learning with bayesian neural networks
Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, and Marcus Rohrbach · 2020
Closest in time.
Continual learning with bayesian neural networks for non-stationary data
Richard Kurle, Botond Cseke, Alexej Klushyn, Patrick van der Smagt, and Stephan Günnemann · 2020
Closest in time.
A neural dirichlet process mixture model for task-free continual learning
Soochan Lee, Junsoo Ha, Dongsu Zhang, and Gunhee Kim · 2020
Closest in time.