% Encoding: UTF-8 @String{ACM = {Association for Computing Machinery}} @String{AISTATS = {International Conference on Artificial Intelligence and Statistics (AISTATS)}} @String{AMS = {American Mathematical Society}} @String{ANM = {Applied Numerical Mathematics}} @String{AnnProb = {The Annals of Probability}} @String{AnnStat = {The Annals of Statistics}} @String{APS = {American Physical Society}} # Preprints @String{ARXIV = {arXiv}} @String{BayesAna = {Bayesian Analysis}} @String{CISS = {Annual Conference on Information Sciences and Systems (CISS)}} @String{CUP = {Cambridge University Press}} @String{ECP = {Electronic Communications in Probability}} @String{Elsevier = {Elsevier}} @String{FCM = {Foundations of Computational Mathematics}} @String{ICLR = {International Conference on Learning Representations (ICLR)}} @String{ICML = {International Conference on Machine Learning (ICML)}} @String{IEEE = {IEEE}} @String{IMAJAM = {IMA Journal of Applied Mathematics}} @String{IMAJNA = {IMA Journal of Numerical Analysis}} @String{JACM = {Journal of the ACM}} @String{JASA = {Journal of the American Statistical Association}} @String{JCP = {Journal of Computational Physics}} @String{JFAA = {Journal of Fourier Analysis and Applications}} # Journals @String{JMLR = {Journal of Machine Learning Research}} @String{JRSSA = {Journal of the Royal Statistical Society: Series A (Statistics in Society)}} @String{JRSSB = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)}} @String{JRSSC = {Journal of the Royal Statistical Society: Series C (Applied Statistics)}} @String{LAA = {Linear Algebra and its Applications}} @String{MIT = {MIT Press}} @String{NeurComp = {Neural Computation}} # Conferences @String{NeurIPS = {Advances in Neural Information Processing Systems (NeurIPS)}} @String{Psym = {Psychometrika}} @String{PUP = {Princeton University Press}} @String{RevModPhys = {Reviews of Modern Physics}} @String{SIAM = {Society for Industrial and Applied Mathematics}} @String{SIAP = {SIAM Journal on Applied Mathematics}} @String{SICOMP = {SIAM Journal on Computing}} @String{SIMAX = {SIAM Journal on Matrix Analysis and Applications}} @String{SIMMS = {Multiscale Modeling and Simulation}} @String{SINUM = {SIAM Journal on Numerical Analysis}} @String{SIOPT = {SIAM Journal on Optimization}} @String{SIREV = {SIAM Review}} @String{SISC = {SIAM Journal on Scientific Computing}} # Publishers @String{Springer = {Springer}} @String{StatComp = {Statistics and Computing}} @String{StatSci = {Statistical Science}} @String{TF = {Taylor \& Francis}} @String{UAI = {Conference on Uncertainty in Artificial Intelligence (UAI)}} @book{Golub2013, author = {Golub, Gene H. and van Loan, Charles F.}, publisher = {Johns Hopkins University Press}, title = {Matrix Computations}, year = 2013 } @book{Hennig2022, author = {Hennig, Philipp and Osborne, Michael A. and Kersting, Hans P.}, doi = {10.1017/9781316681411}, isbn = 9781316681411, publisher = CUP, title = {{P}robabilistic {N}umerics: Computation as Machine Learning}, year = 2022 } @article{Kirkpatrick2017, author = {Kirkpatrick, James and Pascanu, Razvan and Rabinowitz, Neil and Veness, Joel and Desjardins, Guillaume and Rusu, Andrei A. and Milan, Kieran and Quan, John and Ramalho, Tiago and Grabska-Barwinska, Agnieszka and Hassabis, Demis and Clopath, Claudia and Kumaran, Dharshan and Hadsell, Raia}, title = {Overcoming catastrophic forgetting in neural networks}, journal = {Proceedings of the National Academy of Sciences}, year = 2017, volume = 114, number = 13, pages = {3521--3526}, publisher = {Proceedings of the National Academy of Sciences} } @inproceedings{titsias2020functional, title = {Functional Regularisation for Continual Learning with {G}aussian Processes}, author = {Michalis K. 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Cathey}, title = {The iterated {K}alman filter update as a {G}auss--{N}ewton method}, journal = {IEEE Transaction on Automatic Control}, year = 1993, volume = 38, number = 2, pages = {294--297} } @ARTICLE{Schraudolph2002, author = {Schraudolph, Nicol N.}, journal = {Neural Computation}, title = {Fast Curvature Matrix-Vector Products for Second-Order Gradient Descent}, year = 2002, volume = 14, number = 7, pages = {1723-1738}, doi = {10.1162/08997660260028683} } @INPROCEEDINGS{8545895, author={Liu, Xialei and Masana, Marc and Herranz, Luis and Van de Weijer, Joost and López, Antonio M. and Bagdanov, Andrew D.}, booktitle={2018 24th International Conference on Pattern Recognition (ICPR)}, title={Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting}, year={2018}, volume={}, number={}, pages={2262-2268}, keywords={Task analysis;Training;Training data;Neural networks;Data models;Computer vision;Standards}, doi={10.1109/ICPR.2018.8545895}} @InProceedings{pmlr-v80-schwarz18a, title = {Progress \& Compress: A scalable framework for continual learning}, author = {Schwarz, Jonathan and Czarnecki, Wojciech and Luketina, Jelena and Grabska-Barwinska, Agnieszka and Teh, Yee Whye and Pascanu, Razvan and Hadsell, Raia}, booktitle = {Proceedings of the 35th International Conference on Machine Learning}, pages = {4528--4537}, year = {2018}, editor = {Dy, Jennifer and Krause, Andreas}, volume = {80}, series = {Proceedings of Machine Learning Research}, month = {10--15 Jul}, publisher = {PMLR}, } @INPROCEEDINGS {9009502, author = {D. Park and S. Hong and B. Han and K. Lee}, booktitle = {2019 IEEE/CVF International Conference on Computer Vision (ICCV)}, title = {Continual Learning by Asymmetric Loss Approximation With Single-Side Overestimation}, year = {2019}, volume = {}, issn = {}, pages = {3334-3343}, abstract = {Catastrophic forgetting is a critical challenge in training deep neural networks. Although continual learning has been investigated as a countermeasure to the problem, it often suffers from the requirements of additional network components and the limited scalability to a large number of tasks. We propose a novel approach to continual learning by approximating a true loss function using an asymmetric quadratic function with one of its sides overestimated. Our algorithm is motivated by the empirical observation that the network parameter updates affect the target loss functions asymmetrically. In the proposed continual learning framework, we estimate an asymmetric loss function for the tasks considered in the past through a proper overestimation of its unobserved sides in training new tasks, while deriving the accurate model parameter for the observable sides. In contrast to existing approaches, our method is free from the side effects and achieves the state-of-the-art accuracy that is even close to the upper-bound performance on several challenging benchmark datasets.}, keywords = {task analysis;training;neural networks;approximation algorithms;optimization;batch production systems;scalability}, doi = {10.1109/ICCV.2019.00343}, url = {https://doi.ieeecomputersociety.org/10.1109/ICCV.2019.00343}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, month = {nov} } @inproceedings{NIPS2017_f708f064, author = {Lee, Sang-Woo and Kim, Jin-Hwa and Jun, Jaehyun and Ha, Jung-Woo and Zhang, Byoung-Tak}, booktitle = {Advances in Neural Information Processing Systems}, editor = {I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett}, pages = {}, publisher = {Curran Associates, Inc.}, title = {Overcoming Catastrophic Forgetting by Incremental Moment Matching}, url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/f708f064faaf32a43e4d3c784e6af9ea-Paper.pdf}, volume = {30}, year = {2017} } @Misc{Dohare2023, author = {Dohare, Shibhansh and Hernandez-Garcia, J. Fernando and Rahman, Parash and Mahmood, A. Rupam and Sutton, Richard S.}, title = {{Maintaining Plasticity in Deep Continual Learning}}, year = {2023}, eprint = {2306.13812}, archiveprefix = {arXiv}, } @article{deng2012mnist, title={The mnist database of handwritten digit images for machine learning research}, author={Deng, Li}, journal={IEEE Signal Processing Magazine}, volume={29}, number={6}, pages={141--142}, year={2012}, publisher={IEEE} } @software{jax2018github, author = {James Bradbury and Roy Frostig and Peter Hawkins and Matthew James Johnson and Chris Leary and Dougal Maclaurin and George Necula and Adam Paszke and Jake Vander{P}las and Skye Wanderman-{M}ilne and Qiao Zhang}, title = {{JAX}: composable transformations of {P}ython+{N}um{P}y programs}, url = {http://github.com/google/jax}, version = {0.3.13}, year = {2018}, } @misc{munoz2019, title={Continual Multi-task Gaussian Processes}, author={Pablo Moreno-Muñoz and Antonio Artés-Rodríguez and Mauricio A. Álvarez}, year={2019}, eprint={1911.00002}, archivePrefix={arXiv} } @InProceedings{kapoor2021, title = {Variational Auto-Regressive Gaussian Processes for Continual Learning}, author = {Kapoor, Sanyam and Karaletsos, Theofanis and Bui, Thang D}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {5290--5300}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month = {18--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v139/kapoor21b/kapoor21b.pdf}, url = {https://proceedings.mlr.press/v139/kapoor21b.html}, abstract = {Through sequential construction of posteriors on observing data online, Bayes’ theorem provides a natural framework for continual learning. We develop Variational Auto-Regressive Gaussian Processes (VAR-GPs), a principled posterior updating mechanism to solve sequential tasks in continual learning. By relying on sparse inducing point approximations for scalable posteriors, we propose a novel auto-regressive variational distribution which reveals two fruitful connections to existing results in Bayesian inference, expectation propagation and orthogonal inducing points. Mean predictive entropy estimates show VAR-GPs prevent catastrophic forgetting, which is empirically supported by strong performance on modern continual learning benchmarks against competitive baselines. A thorough ablation study demonstrates the efficacy of our modeling choices.} } @inproceedings{Titsias2020, author = {Michalis K. Titsias and Jonathan Schwarz and Alexander G. de G. Matthews and Razvan Pascanu and Yee Whye Teh}, title = {Functional Regularisation for Continual Learning with Gaussian Processes}, booktitle = {8th International Conference on Learning Representations, {ICLR} 2020, Addis Ababa, Ethiopia, April 26-30, 2020}, publisher = {OpenReview.net}, year = {2020}, url = {https://openreview.net/forum?id=HkxCzeHFDB}, timestamp = {Thu, 07 May 2020 17:11:48 +0200}, biburl = {https://dblp.org/rec/conf/iclr/TitsiasSMPT20.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } @misc{mirzadeh2020, title={Linear Mode Connectivity in Multitask and Continual Learning}, author={Seyed Iman Mirzadeh and Mehrdad Farajtabar and Dilan Gorur and Razvan Pascanu and Hassan Ghasemzadeh}, year={2021}, booktitle = {International Conference on Learning Representations}, eprint={2010.04495}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2010.04495}, } @article{adel2024, author = {Adel, Tameem}, year = {2024}, month = {06}, pages = {377-417}, title = {Similarity-Based Adaptation for Task-Aware and Task-Free Continual Learning}, volume = {80}, journal = {Journal of Artificial Intelligence Research}, doi = {10.1613/jair.1.15693} } @misc{ramesh2022, title={Model Zoo: A Growing "Brain" That Learns Continually}, author={Rahul Ramesh and Pratik Chaudhari}, year={2022}, eprint={2106.03027}, archivePrefix={arXiv}, booktitle = {International Conference on Learning Representations}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2106.03027}, } @inproceedings{wang2022, author = {Wang, Liyuan and Zhang, Xingxing and Li, Qian and Zhu, Jun and Zhong, Yi}, title = {CoSCL: Cooperation of Small Continual Learners is Stronger Than Big One}, year = {2022}, isbn = {978-3-031-19808-3}, publisher = {Springer-Verlag}, doi = {10.1007/978-3-031-19809-0_15}, booktitle = {Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXVI}, pages = {254–271}, numpages = {18} } @Comment{jabref-meta: databaseType:biblatex;}