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Adapting Auxiliary Losses Using Gradient Similarity, , , , и . (2018)cite arxiv:1812.02224.Multiplicative Interactions and Where to Find Them., , , , , , , , и . ICLR, OpenReview.net, (2020)Maxwell's Demon at Work: Efficient Pruning by Leveraging Saturation of Neurons., , , , , и . CoRR, (2024)Predicting Unreliable Predictions by Shattering a Neural Network., , , , , и . CoRR, (2021)Probing Transfer in Deep Reinforcement Learning without Task Engineering., , , , и . CoLLAs, том 199 из Proceedings of Machine Learning Research, стр. 1231-1254. PMLR, (2022)Improving the Gating Mechanism of Recurrent Neural Networks., , , , и . ICML, том 119 из Proceedings of Machine Learning Research, стр. 3800-3809. PMLR, (2020)When Does Re-initialization Work?, , , , , , и . ICBINB, том 187 из Proceedings of Machine Learning Research, стр. 12-26. PMLR, (2022)Spectral Normalisation for Deep Reinforcement Learning: An Optimisation Perspective., , , , , и . ICML, том 139 из Proceedings of Machine Learning Research, стр. 3734-3744. PMLR, (2021)A simple neural network module for relational reasoning., , , , , , и . NIPS, стр. 4967-4976. (2017)Theano: A CPU and GPU Math Compiler in Python., , , , , , , , и . SciPy, стр. 18-24. scipy.org, (2010)