February 18, 2019 article First-order methods almost always avoid strict saddle points Mathematical Programming DOI: 10.1007/s10107-019-01374-3 OpenAlex Authors 0 Author list not loaded yet. Cited by 5 stored of 150 Search Sort Most cited Newest Oldest Patent citations Title Any typearticle review book-chapter conference-paper preprint dissertation book dataset other Any fieldAgricultural and Biological Sciences Arts and Humanities Biochemistry, Genetics and Molecular Biology Business, Management and Accounting Chemical Engineering Chemistry Computer Science Decision Sciences Dentistry Earth and Planetary Sciences Economics, Econometrics and Finance Energy Engineering Environmental Science Health Professions Immunology and Microbiology Materials Science Mathematics Medicine Neuroscience Nursing Pharmacology, Toxicology and Pharmaceutics Physics and Astronomy Psychology Social Sciences Veterinary Open access Only strict saddles in the energy landscape of predictive coding networks? * 2025 Journal of Statistical Mechanics Theory and Experiment article Computer Science Neural Networks and Applications Open access 1 citations Energy landscapes of combinatorial optimization in Ising machines 2024 Physical review. E article Computer Science Quantum Computing Algorithms and Architecture Open access 18 citations Convergence of gradient descent for learning linear neural networks 2024 Advances in Continuous and Discrete Models article Computer Science Stochastic Gradient Optimization Techniques Open access 10 citations The loss landscape of deep linear neural networks: a second-order analysis 2021 arXiv (Cornell University) preprint Computer Science Stochastic Gradient Optimization Techniques Open access 1 citations Learning deep linear neural networks: Riemannian gradient flows and convergence to global minimizers 2021 Information and Inference A Journal of the IMA article Computer Science Stochastic Gradient Optimization Techniques Open access 29 citations 5 results References 0