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Continual learning is a subfield of machine learning, which aims to allow machine learning models to continuously learn on new data, by accumulating knowledge without forgetting what was learned in the past.
Self-improving reactive agents based on reinforcement learning, planning and teaching
Long-Ji Lin · 1992
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Adapting bias by gradient descent: An incremental version of delta-bar-delta
Richard S Sutton · 1992
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Catastrophic forgetting in connectionist networks
Robert M French · 1999
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On the role of tracking in stationary environments
Richard S Sutton, Anna Koop, and David Silver · 2007
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Sample-based learning and search with permanent and transient memories
David Silver, Richard S Sutton, and Martin Müller · 2008
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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
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Active learning literature survey
Burr Settles · 2009
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Impossibility theorems for domain adaptation
Shai Ben David, Tyler Lu, Teresa Luu, and Dávid Pál · 2010
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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A PAC-bayesian bound for lifelong learning
Anastasia Pentina and Christoph H. Lampert · 2014
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Towards open world recognition
Abhijit Bendale and Terrance Boult · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
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Regulation (eu) 2016/679 of the european parliament and of the council of 27 april 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing directive 95/46/ec (general data protection regulation)
European Union · 2016
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Prioritized experience replay
Tom Schaul, John Quan andIoannis Antonoglou, and David Silver · 2016
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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
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Learning without forgetting
Zhizhong Li and Derek Hoiem · 2017
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Neural episodic control
Alexander Pritzel, Benigno Uria, Sriram Srinivasan, Adria Puigdomenech Badia, Oriol Vinyals, Demis Hassabis, Daan Wierstra, and Charles Blundell · 2017
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Reinforcement Learning: An Introduction
Richard S. Sutton and Andrew G. Barto · 2018
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Taskonomy: Disentangling task transfer learning
Amir R Zamir, Alexander Sax, William Shen, Leonidas J Guibas, Jitendra Malik, and Silvio Savarese · 2018
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Scibert: A pretrained language model for scientific text
Iz Beltagy, Kyle Lo, and Arman Cohan · 2019
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Parameter-efficient transfer learning for NLP
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly · 2019
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Unmasking clever hans predictors and assessing what machines really learn
Sebastian Lapuschkin, Stephan Wäldchen, Alexander Binder, Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller · 2019
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Experience replay for continual learning
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lillicrap, and Gregory Wayne · 2019
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Tasks without borders: A new approach to online multi-task learning
Alexander Zimin and Christoph H. Lampert · 2019
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On warm-starting neural network training
Jordan Ash and Ryan P Adams · 2020
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Uncertainty-guided continual learning with bayesian neural networks
Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, and Marcus Rohrbach · 2020
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Let’s agree to agree: Neural networks share classification order on real datasets
Guy Hacohen, Leshem Choshen, and Daphna Weinshall · 2020
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Embracing change: Continual learning in deep neural networks
Raia Hadsell, Dushyant Rao, Andrei A Rusu, and Razvan Pascanu · 2020
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A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability
Xiaowei Huang, Daniel Kroening, Wenjie Ruan, James Sharp, Youcheng Sun, Emese Thamo, Min Wu, and Xinping Yi · 2020
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Optimal continual learning has perfect memory and is np-hard
Jeremias Knoblauch, Hisham Husain, and Tom Diethe · 2020
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Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Sergey Levine, Aviral Kumar, George Tucker, and Justin fu · 2020
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Mastering atari, go, chess and shogi by planning with a learned model
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, et al · 2020
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Green AI
Roy Schwartz, Jesse Dodge, Noah A Smith, and Oren Etzioni · 2020
Cited alongside, same era.
A comprehensive survey on transfer learning
Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, and Qing He · 2020
Cited alongside, same era.
What is MLOPs?
Sridhar Alla, Suman Kalyan Adari, Sridhar Alla, and Suman Kalyan Adari · 2021
Cited alongside, same era.
A study on the plasticity of neural networks
Tudor Berariu, Wojciech Czarnecki, Soham De, Jorg Bornschein, Samuel Smith, Razvan Pascanu, and Claudia Clopath · 2021
Cited alongside, same era.
Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods
E. Hüllermeier and W. Waegeman · 2021
Cited alongside, same era.
Achieving forgetting prevention and knowledge transfer in continual learning
CLEVA-compass: A continual learning evaluation assessment compass to promote research transparency and comparability
Martin Mundt, Steven Lang, Quentin Delfosse, and Kristian Kersting · 2022
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How to measure uncertainty in uncertainty sampling for active learning
V.L. Nguyen, M.H. Shaker, and E. Hüllermeier · 2022
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Foundational models for continual learning: An empirical study of latent replay, 2022
Oleksiy Ostapenko, Timothee Lesort, Pau Rodríguez, Md Rifat Arefin, Arthur Douillard, Irina Rish, and Laurent Charlin · 2022
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When Deep Classifiers Agree: Analyzing Correlations Between Learning Order and Image Statistics
Iuliia Pliushch, Martin Mundt, Nicolas Lupp, and Visvanathan Ramesh · 2022
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A theory for knowledge transfer in continual learning
Diana Benavides Prado and Patricia Riddle · 2022
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Zixuan Ke, Bing Liu, Nianzu Ma, Hu Xu, and Lei Shu · 2021
Cited alongside, same era.
Mind the gap: Assessing temporal generalization in neural language models
Angeliki Lazaridou, Adhi Kuncoro, Elena Gribovskaya, Devang Agrawal, Adam Liska, Tayfun Terzi, Mai Gimenez, Cyprien de Masson d’Autume, Tomas Kocisky, Sebastian Ruder, et al · 2021
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
Cited alongside, same era.
Continuous transition: Improving sample efficiency for continuous control problems via mixup
Junfan Lin, Zhongzhan Huang, Keze Wang, Xiaodan Liang, Weiwei Chen, and Liang Lin · 2021
Cited alongside, same era.
Linear mode connectivity in multitask and continual learning
Seyed Iman Mirzadeh, Mehrdad Farajtabar, Dilan Gorur, Razvan Pascanu, and Hassan Ghasemzadeh · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Cited alongside, same era.
Model zoo: A growing" brain" that learns continually
Rahul Ramesh and Pratik Chaudhari · 2021
Cited alongside, same era.
Three types of incremental learning
Gido M van de Ven, Tinne Tuytelaars, and Andreas S Tolias · 2022
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Disentangling transfer in continual reinforcement learning
Maciej Wolczyk, Michał Zając, Razvan Pascanu, Łukasz Kuciński, and Piotr Miłoś · 2022
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Robust fine-tuning of zero-shot models
Mitchell Wortsman, Gabriel Ilharco, Jong Wook Kim, Mike Li, Simon Kornblith, Rebecca Roelofs, Raphael Gontijo Lopes, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, et al · 2022
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The application of the right to be forgotten in the machine learning context: From the perspective of european laws
Zeyu Zhao · 2022
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A definition of continual reinforcement learning
David Abel, André Barreto, Benjamin Van Roy, Doina Precup, Hado van Hasselt, and Satinder Singh · 2023
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AI and compute
Dario Amodei and Danny Hernandez · 2023
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Towards realistic evaluation of industrial continual learning scenarios with an emphasis on energy consumption and computational footprint
Vivek Chavan, Paul Koch, Marian Schlüter, and Clemens Briese · 2023
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Personalization of CTC speech recognition models
Saket Dingliwal, Monica Sunkara, Srikanth Ronanki, Jeff Farris, Katrin Kirchhoff, and Sravan Bodapati · 2023
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Loss of plasticity in deep continual learning
Shibhansh Dohare, Juan Hernandez-Garcia, Parash Rahman, Richard Sutton, and Rupam Mahmood · 2023
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Real-time evaluation in online continual learning: A new hope
Yasir Ghunaim, Adel Bibi, Kumail Alhamoud, Motasem Alfarra, Hasan Abed Al Kader Hammoud, Ameya Prabhu, Philip HS Torr, and Bernard Ghanem · 2023
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A roomba recorded a woman on the toilet. how did screenshots end up on facebook?
Eileen Guo · 2023
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Real-time machine learning: challenges and solutions, Jan 2022
Chip Huyen · 2023
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Scalable real-time recurrent learning using columnar-constructive networks
Khurram Javed, Haseeb Shah, Richard S Sutton, and Martha White · 2023
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Generating instance-level prompts for rehearsal-free continual learning
Dahuin Jung, Dongyoon Han, Jihwan Bang, and Hwanjun Song · 2023
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Incdsi: incrementally updatable document retrieval
Varsha Kishore, Chao Wan, Justin Lovelace, Yoav Artzi, and Kilian Q Weinberger · 2023
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Retraining model during deployment: Continuous training and continuous testing, 2023
Akinwande Komolafe · 2023
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Machine learning operations (MLOPS): Overview, definition, and architecture
Dominik Kreuzberger, Niklas Kühl, and Sebastian Hirschl · 2023
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Uncovering design principles for lifelong learning ai accelerators
Dhireesha Kudithipudi, Anurag Daram, Abdullah Zyarah, Fatima tuz Zohora, James B. Aimone, Angel Yanguas-Gil, Nicholas Soures, Emre Neftci, Matthew Mattina, Vincenzo Lomonaco, Clare D. Thiem, and Benjamin Epstein · 2023
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Continual learning as computationally constrained reinforcement learning
Saurabh Kumar, Henrik Marklund, Ashish Rao, Yifan Zhu, Hong Jun Jeon, Yueyang Liu, and Benjamin Van Roy · 2023
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Steering prototype with prompt-tuning for rehearsal-free continual learning
Zhuowei Li, Long Zhao, Zizhao Zhang, Han Zhang, Di Liu, Ting Liu, and Dimitris N Metaxas · 2023
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A wholistic view of continual learning with deep neural networks: Forgotten lessons and the bridge to active and open world learning
Martin Mundt, Yongwon Hong, Iuliia Pliushch, and Visvanathan Ramesh · 2023
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Dinov2: Learning robust visual features without supervision
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al · 2023
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First session adaptation: A strong replay-free baseline for class-incremental learning
Aristeidis Panos, Yuriko Kobe, Daniel Olmeda Reino, Rahaf Aljundi, and Richard E Turner · 2023
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Deep Continual Learning (Dagstuhl Seminar 23122)
Tinne Tuytelaars, Bing Liu, Vincenzo Lomonaco, Gido van de Ven, and Andrea Cossu · 2023
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A comprehensive survey of continual learning: Theory, method and application
Liyuan Wang, Xingxing Zhang, Hang Su, and Jun Zhu · 2023
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Language models meet world models: Embodied experiences enhance language models
Jiannan Xiang, Tianhua Tao, Yi Gu, Tianmin Shu, Zirui Wang, Zichao Yang, and Zhiting Hu · 2023
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Deep class-incremental learning: A survey
Da-Wei Zhou, Qi-Wei Wang, Zhi-Hong Qi, Han-Jia Ye, De-Chuan Zhan, and Ziwei Liu · 2023
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