Pages that link to "Item:Q5214225"

The following pages link to Spurious Valleys in Two-layer Neural Network Optimization Landscapes (Q5214225):

Displaying 20 items.

• On the landscape of one-hidden-layer sparse networks and beyond (Q2152502) (← links)
• Landscape analysis for shallow neural networks: complete classification of critical points for affine target functions (Q2156337) (← links)
• Optimization for deep learning: an overview (Q2218095) (← links)
• Learning the mapping \(\mathbf{x}\mapsto \sum\limits_{i=1}^d x_i^2\): the cost of finding the needle in a haystack (Q2667355) (← links)
• (Q3358260) (← links)
• Multilinear Compressive Sensing and an Application to Convolutional Linear Networks (Q5025785) (← links)
• On PDE Characterization of Smooth Hierarchical Functions Computed by Neural Networks (Q5034481) (← links)
• (Q5053289) (← links)
• Nonlinear Weighted Directed Acyclic Graph and A Priori Estimates for Neural Networks (Q5081927) (← links)
• On the Benefit of Width for Neural Networks: Disappearance of Basins (Q5097010) (← links)
• (Q5159413) (← links)
• A machine learning approach to portfolio pricing and risk management for high‐dimensional problems (Q6054432) (← links)
• Spurious Valleys, NP-Hardness, and Tractability of Sparse Matrix Factorization with Fixed Support (Q6101122) (← links)
• Certifying the Absence of Spurious Local Minima at Infinity (Q6116236) (← links)
• How many neurons do we need? A refined analysis for shallow networks trained with gradient descent (Q6592785) (← links)
• Exact solutions of a deep linear network (Q6611441) (← links)
• The landscape of the planted clique problem: dense subgraphs and the overlap gap property (Q6616866) (← links)
• On the omnipresence of spurious local minima in certain neural network training problems (Q6629537) (← links)
• On the existence of minimizers in shallow residual ReLU neural network optimization landscapes (Q6652417) (← links)
• Gradient descent provably escapes saddle points in the training of shallow ReLU networks (Q6655804) (← links)