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Reverse engineering learned optimizers reveals known and novel mechanisms., , , , и . CoRR, (2020)Practical Tradeoffs between Memory, Compute, and Performance in Learned Optimizers., , , , и . CoLLAs, том 199 из Proceedings of Machine Learning Research, стр. 142-164. PMLR, (2022)Finite Versus Infinite Neural Networks: an Empirical Study., , , , , , и . NeurIPS, (2020)Adversarial Examples that Fool both Computer Vision and Time-Limited Humans., , , , , , и . NeurIPS, стр. 3914-3924. (2018)Score-Based Generative Modeling through Stochastic Differential Equations., , , , , и . ICLR, OpenReview.net, (2021)Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling., , , и . ICML, том 162 из Proceedings of Machine Learning Research, стр. 8926-8945. PMLR, (2022)Guided evolutionary strategies: augmenting random search with surrogate gradients., , , , и . ICML, том 97 из Proceedings of Machine Learning Research, стр. 4264-4273. PMLR, (2019)Generalizing Hamiltonian Monte Carlo with Neural Networks., , и . ICLR (Poster), OpenReview.net, (2018)Adversarial Reprogramming of Neural Networks., , и . ICLR (Poster), OpenReview.net, (2019)Small-scale proxies for large-scale Transformer training instabilities., , , , , , , , , и 6 other автор(ы). ICLR, OpenReview.net, (2024)