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Steepest Descent Neural Architecture Optimization: Escaping Local Optimum with Signed Neural Splitting.

, , , , and . CoRR, (2020)

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On the Power of Over-parametrization in Neural Networks with Quadratic Activation., and . ICML, volume 80 of Proceedings of Machine Learning Research, page 1328-1337. PMLR, (2018)Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima., , , , and . ICML, volume 80 of Proceedings of Machine Learning Research, page 1338-1347. PMLR, (2018)Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes, , , and . (2019)cite arxiv:1908.00261Comment: Additional references and discussion of prior work.Kernel and Rich Regimes in Overparametrized Models., , , , , , , and . COLT, volume 125 of Proceedings of Machine Learning Research, page 3635-3673. PMLR, (2020)Distributed Stochastic Variance Reduced Gradient Methods., , and . CoRR, (2015)Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks., , and . CoRR, (2023)LoRA Training in the NTK Regime has No Spurious Local Minima., , and . CoRR, (2024)Scaling In-Context Demonstrations with Structured Attention., , , and . CoRR, (2023)Settling the Sample Complexity of Online Reinforcement Learning., , , and . CoRR, (2023)Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models., , , and . CoRR, (2023)