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Local Saddle Point Optimization: A Curvature Exploitation Approach.

, , , and . AISTATS, volume 89 of Proceedings of Machine Learning Research, page 486-495. PMLR, (2019)

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Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm., , , and . ICML, volume 32 of JMLR Workshop and Conference Proceedings, page 793-801. JMLR.org, (2014)Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion., , , , , and . CoRR, (2023)Batch normalization provably avoids ranks collapse for randomly initialised deep networks., , , , and . NeurIPS, (2020)Estimating Diffusion Networks: Recovery Conditions, Sample Complexity and Soft-thresholding Algorithm., , , and . J. Mach. Learn. Res., (2016)Exponential convergence rates for Batch Normalization: The power of length-direction decoupling in non-convex optimization., , , , , and . AISTATS, volume 89 of Proceedings of Machine Learning Research, page 806-815. PMLR, (2019)Optimization for Neural Networks: Quest for Theoretical Understandings.. ETH Zurich, Zürich, Switzerland, (2020)base-search.net (ftethz:oai:www.research-collection.ethz.ch:20.500.11850/421981).On the impact of activation and normalization in obtaining isometric embeddings at initialization., , and . CoRR, (2023)Efficient displacement convex optimization with particle gradient descent., , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 6836-6854. PMLR, (2023)On Bridging the Gap between Mean Field and Finite Width Deep Random Multilayer Perceptron with Batch Normalization., , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 15388-15400. PMLR, (2023)Rethinking the Variational Interpretation of Accelerated Optimization Methods., , and . NeurIPS, page 14396-14406. (2021)