42 citations · 137 across the 20 of their papers we have counts for
7 papers · 1 filter
Revealing Spatial-temporal Taxi Demand Patterns after Vaccination in COVID-19 Pandemic
Zihao Li, Cheng Zhang, Xiaoqiang Kong +2
The COVID-19 pandemic has had an unprecedented impact on our daily lives. With the increase in vaccination rate, normalcy gradually returns, so is the taxi demand. However, the cha…
The Asymmetric Maximum Margin Bias of Quasi-Homogeneous Neural Networks
Daniel Kunin, Atsushi Yamamura, Chao Ma +1
In this work, we explore the maximum-margin bias of quasi-homogeneous neural networks trained with gradient flow on an exponential loss and past a point of separability. We introdu…
Correcting Convexity Bias in Function and Functional Estimate
Chao Ma, Lexing Ying
A general framework with a series of different methods is proposed to improve the estimate of convex function (or functional) values when only noisy observations of the true input…
Generalization Error Bounds for Deep Neural Networks Trained by SGD
Mingze Wang, Chao Ma
Generalization error bounds for deep neural networks trained by stochastic gradient descent (SGD) are derived by combining a dynamical control of an appropriate parameter norm and…
Early Stage Convergence and Global Convergence of Training Mildly Parameterized Neural Networks
Mingze Wang, Chao Ma
The convergence of GD and SGD when training mildly parameterized neural networks starting from random initialization is studied. For a broad range of models and loss functions, inc…
Beyond the Quadratic Approximation: the Multiscale Structure of Neural Network Loss Landscapes
Chao Ma, Daniel Kunin, Lei Wu +1
A quadratic approximation of neural network loss landscapes has been extensively used to study the optimization process of these networks. Though, it usually holds in a very small…