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Improving Gibbs Sampler Scan Quality with DoGS., и . ICML, том 70 из Proceedings of Machine Learning Research, стр. 2469-2477. PMLR, (2017)Connections between Support Vector Machines, Wasserstein distance and gradient-penalty GANs, и . (2019)cite arxiv:1910.06922Comment: Code at https://github.com/AlexiaJM/MaximumMarginGANs.Gotta Go Fast When Generating Data with Score-Based Models., , , , и . CoRR, (2021)Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity., , , , и . NeurIPS, стр. 19095-19108. (2021)No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths., , , и . ICML, OpenReview.net, (2024)A Modern Take on the Bias-Variance Tradeoff in Neural Networks., , , , , , и . CoRR, (2018)Neural Networks Efficiently Learn Low-Dimensional Representations with SGD., , , , и . CoRR, (2022)A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Differentiable Games., , , и . AISTATS, том 108 из Proceedings of Machine Learning Research, стр. 2863-2873. PMLR, (2020)A Study of Condition Numbers for First-Order Optimization., , , и . AISTATS, том 130 из Proceedings of Machine Learning Research, стр. 1261-1269. PMLR, (2021)Linear Lower Bounds and Conditioning of Differentiable Games., , , и . ICML, том 119 из Proceedings of Machine Learning Research, стр. 4583-4593. PMLR, (2020)