Loss Landscape Characterization of Neural Networks without Over-Parametrization
Optimization methods play a crucial role in modern machine learning, powering the
remarkable empirical achievements of deep learning models. These successes are even …
remarkable empirical achievements of deep learning models. These successes are even …
Understanding Adam Requires Better Rotation Dependent Assumptions
Despite its widespread adoption, Adam's advantage over Stochastic Gradient Descent
(SGD) lacks a comprehensive theoretical explanation. This paper investigates Adam's …
(SGD) lacks a comprehensive theoretical explanation. This paper investigates Adam's …
Understanding Adam Requires Better Rotation Dependent Assumptions
TH Zhang, L Maes, A Jolicoeur-Martineau… - OPT 2024: Optimization … - openreview.net
Despite its widespread adoption, Adam's advantage over Stochastic Gradient Descent
(SGD) lacks a comprehensive theoretical explanation. This paper investigates Adam's …
(SGD) lacks a comprehensive theoretical explanation. This paper investigates Adam's …
[PDF][PDF] ACCELERATING TRAINING WITH NEURON INTERACTION AND NOWCASTING NETWORKS
Neural network training can be accelerated when a learnable update rule is used in lieu of
classic adaptive optimizers (eg Adam). However, learnable update rules can be costly and …
classic adaptive optimizers (eg Adam). However, learnable update rules can be costly and …