activity
20122023
most citedTowards Understanding the Spectral Bias of Deep Learning

65 citations · 245 across the 28 of their papers we have counts for

collaborators
Showing 2020Show all

8 papers · 1 filter

cs.LG2020★ 6 cited

Unsupervised Domain Adaptation for Visual Navigation

Shangda Li, Devendra Singh Chaplot, Yao-Hung Hubert Tsai +3

Advances in visual navigation methods have led to intelligent embodied navigation agents capable of learning meaningful representations from raw RGB images and perform a wide varie…

eess.IV2020★ 7 cited

Claw U-Net: A Unet-based Network with Deep Feature Concatenation for Scleral Blood Vessel Segmentation

Chang Yao, Jingyu Tang, Menghan Hu +4

Sturge-Weber syndrome (SWS) is a vascular malformation disease, and it may cause blindness if the patient's condition is severe. Clinical results show that SWS can be divided into…

math.FA2020★ 6 cited

Fractional Fourier transforms on and applications

Wei Chen, Zunwei Fu, Loukas Grafakos +1

This paper is devoted to the theory of the fractional Fourier transform (FRFT) for . In view of the special structure of the FRFT, we study FRFT proper…

cs.IT2020

Signal Encryption Strategy based on Domain change of the Fractional Fourier Transform

Wei Chen, Zunwei Fu, Loukas Grafakos +1

This paper provides a double encryption algorithm that uses the lack of invertibility of the fractional Fourier transform (FRFT) on . One encryption key is a function, which…

math.NA2020★ 1 cited

Semi-implicit Taylor schemes for stiff rough differential equations

Sebastian Riedel, Yue Wu

We study a class of semi-implicit Taylor-type numerical methods that are easy to implement and designed to solve multidimensional stochastic differential equations driven by a gene…

cs.LG2020★ 10 cited

Improving GAN Training with Probability Ratio Clipping and Sample Reweighting

Yue Wu, Pan Zhou, Andrew Gordon Wilson +2

Despite success on a wide range of problems related to vision, generative adversarial networks (GANs) often suffer from inferior performance due to unstable training, especially fo…