1 citations · 1 across the 5 of their papers we have counts for
6 papers
Training Instabilities Induce Flatness Bias in Gradient Descent
Lawrence Wang, Stephen J. Roberts
Classical analyses of gradient descent (GD) define a stability threshold based on the largest eigenvalue of the loss Hessian, often termed sharpness. When the learning rate lies be…
Can Stability be Detrimental? Better Generalization through Gradient Descent Instabilities
Lawrence Wang, Stephen J. Roberts
Traditional analyses of gradient descent optimization show that, when the largest eigenvalue of the loss Hessian - often referred to as the sharpness - is below a critical learning…
The instabilities of large learning rate training: a loss landscape view
Lawrence Wang, Stephen Roberts
Modern neural networks are undeniably successful. Numerous works study how the curvature of loss landscapes can affect the quality of solutions. In this work we study the loss land…
SANE: The phases of gradient descent through Sharpness Adjusted Number of Effective parameters
Lawrence Wang, Stephen J. Roberts
Modern neural networks are undeniably successful. Numerous studies have investigated how the curvature of loss landscapes can affect the quality of solutions. In this work we consi…
HumBugDB: A Large-scale Acoustic Mosquito Dataset
Ivan Kiskin, Marianne Sinka, Adam D. Cobb +13
This paper presents the first large-scale multi-species dataset of acoustic recordings of mosquitoes tracked continuously in free flight. We present 20 hours of audio recordings th…
HumBug Zooniverse: a crowd-sourced acoustic mosquito dataset
Ivan Kiskin, Adam D. Cobb, Lawrence Wang +1
Mosquitoes are the only known vector of malaria, which leads to hundreds of thousands of deaths each year. Understanding the number and location of potential mosquito vectors is of…