iamrp.net-p4qL-1793838287-w540537414 1793838287 Institute for Advanced Materials Research Press info@iamrp.net Institute for Advanced Materials Research Press Journal of Artificial Intelligence for Materials Science J. Artif. Intell. Mater. Sci. 3149-8957 2022 1 1 Maria Hernandez Carlos Vega 2022 10.68159/w540537414 https://iamrp.net/pub/journal/1/article/w540537414 Shurkova EY. Symbolic-connectionist model of relation learning and visual reasoning. Available from: https://era.ed.ac.uk/items/f094402a-bdb3-4cf4-b673-3ae23dba0657/full Kirkpatrick J, Pascanu R, Rabinowitz N, Veness J, Desjardins G, Rusu AA, et al. Overcoming catastrophic forgetting in neural networks. Proc Natl Acad Sci U S A. 2017;114(13):3521-6. Lopez-Paz D, Ranzato MA. Gradient episodic memory for continual learning. Adv Neural Inf Process Syst. 2017;30:1-10. Stern M, Pinson MB, Murugan A. Continual learning of multiple memories in mechanical networks. Phys Rev X. 2020;10(3):031044. Liu H, Yang Y, Wang X. Overcoming catastrophic forgetting in graph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence; 2021. 35(10):8653-61. Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A. Machine learning for molecular and materials science. Nature. 2018;559(7715):547-55. Schmidt J, Marques MR, Botti S, Marques MA. Recent advances and applications of machine learning in solid-state materials science. npj Comput Mater. 2019;5(1):83. Zenke F, Poole B, Ganguli S. Continual learning through synaptic intelligence. In: Proceedings of the 34th International Conference on Machine Learning (ICML). PMLR; 2017. p. 3987-95. Cai J, Xu M, Li W, Xiong Y, Xia W, Tu Z, et al. MeMOT: Multi-object tracking with memory. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2022. p. 8090-100. Go SX, Wang Q, Wang B, Jiang Y, Bajalovic N, Loke DK. Continual learning electrical conduction in resistive-switching-memory materials. Adv Theory Simul. 2022;5(8):2200226. Chang R, Wang YX, Ertekin E. Towards overcoming data scarcity in materials science: Unifying models and datasets with a mixture of experts framework. npj Comput Mater. 2022;8(1):242. Zhang Z, Mondal S, Mandal S, Allred JM, Aghamiri NA, Fali A, et al. Neuromorphic learning with Mott insulator NiO. Proc Natl Acad Sci U S A. 2021;118(39):e2017239118. Liu N, Ihalage A, Zhang H, Giddens H, Yan H, Hao Y. Interactive human-machine learning framework for modelling of ferroelectric-dielectric composites. J Mater Chem C. 2020;8(30):10352-61. Ginart A, Guan M, Valiant G, Zou JY. Making AI forget you: Data deletion in machine learning. Adv Neural Inf Process Syst. 2019;32:1-10. Izzo Z, Smart MA, Chaudhuri K, Zou J. Approximate data deletion from machine learning models. In: Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR; 2021. p. 2008-16. Sekhari A, Acharya J, Kamath G, Suresh AT. Remember what you want to forget: Algorithms for machine unlearning. NeurIPS. 2021;34:18075-86. Bartoldson B, Morcos A, Barbu A, Erlebacher G. The generalization-stability tradeoff in neural network pruning. NeurIPS. 2020;33:20852-64. Yang Z, Yu Y, You C, Steinhardt J, Ma Y. Rethinking bias-variance trade-off for generalization of neural networks. In: International Conference on Machine Learning; 2020. p. 10767-77. Kong K, Li G, Ding M, Wu Z, Zhu C, Ghanem B, et al. Robust optimization as data augmentation for large-scale graphs. CVPR. 2022. p. 60-9. Diethe T, Borchert T, Thereska E, Balle B, Lawrence N. Continual learning in practice. arXiv preprint arXiv:1903.05202. 2019. Wang B, Ouyang Y, Li Y, Pan M, Cui H, Wang Y, et al. MoMa: A simple modular learning framework for material property prediction. In: Fourteenth International Conference on Learning Representations. Bang J, Koh H, Park S, Song H, Ha JW, Choi J. Online continual learning on a contaminated data stream with blurry task boundaries. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2022. p. 9275-84. Wu YC, Feng JW. Development and application of artificial neural network. Wirel Pers Commun. 2018;102(2):1645-56. Parisi GI, Kemker R, Part JL, Kanan C, Wermter S. Continual lifelong learning with neural networks: A review. Neural Netw. 2019;113:54-71. Aljundi R, Babiloni F, Elhoseiny M, Rohrbach M, Tuytelaars T. Memory aware synapses: Learning what (not) to forget. In: Proceedings of the European Conference on Computer Vision (ECCV); 2018. p. 139-54. Schwarz J, Czarnecki W, Luketina J, Grabska-Barwinska A, Teh YW, Pascanu R, et al. Progress & compress: A scalable framework for continual learning. In: International Conference on Machine Learning; 2018. p. 4528-37. Chaudhry A, Ranzato MA, Rohrbach M, Elhoseiny M. Efficient lifelong learning with A-GEM. arXiv preprint arXiv:1812.00420. 2018. Rolnick D, Ahuja A, Schwarz J, Lillicrap T, Wayne G. Experience replay for continual learning. NeurIPS. 2019;32. De Lange M, Aljundi R, Masana M, Parisot S, Jia X, Leonardis A, et al. A continual learning survey: Defying forgetting in classification tasks. IEEE Trans Pattern Anal Mach Intell. 2021;44(7):3366-85.