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Neuro-Inspired Fragmentation and Recall to Overcome Catastrophic Forgetting in Curiosity.

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Boopathy Gandhi Theerthagiri Kuppusamy

 

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Fast Training of Provably Robust Neural Networks by SingleProp., , , , , и . AAAI, стр. 6803-6811. AAAI Press, (2021)Fast Training of Provably Robust Neural Networks by SingleProp., , , , , и . CoRR, (2021)Proper Network Interpretability Helps Adversarial Robustness in Classification., , , , , , и . ICML, том 119 из Proceedings of Machine Learning Research, стр. 1014-1023. PMLR, (2020)Gradient-trained Weights in Wide Neural Networks Align Layerwise to Error-scaled Input Correlations., и . CoRR, (2021)CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks., , , , и . AAAI, стр. 3240-3247. AAAI Press, (2019)CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks., , , , и . CoRR, (2018)Double Descent Demystified: Identifying, Interpreting & Ablating the Sources of a Deep Learning Puzzle., , , , , , , и . CoRR, (2023)Neuro-Inspired Fragmentation and Recall to Overcome Catastrophic Forgetting in Curiosity., , , , , и . CoRR, (2023)Model-agnostic Measure of Generalization Difficulty., , , , , и . ICML, том 202 из Proceedings of Machine Learning Research, стр. 2857-2884. PMLR, (2023)PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach., , , , , , и . ICML, том 97 из Proceedings of Machine Learning Research, стр. 6727-6736. PMLR, (2019)