activity
20122023
most citedTowards Understanding the Spectral Bias of Deep Learning

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

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Showing 2021Show all

12 papers · 1 filter

math.NA2021

A direct imaging method for the exterior and interior inverse scattering problems

Deyue Zhang, Yue Wu, Yinglin Wang +1

This paper is concerned with the inverse acoustic scattering problems by an obstacle or a cavity with a sound-soft or a sound-hard boundary. A direct imaging method relying on the…

eess.IV2021

Unsupervised cross domain learning with applications to 7 layer segmentation of OCTs

Yue Wu, Abraham Olvera Barrios, Ryan Yanagihara +4

Unsupervised cross domain adaptation for OCT 7 layer segmentation and other medical applications where labeled training data is only available in a source domain and unavailable in…

math.FA2021★ 4 cited

Riesz transform associated with the fractional Fourier transform and applications in image edge detection

Zunwei Fu, Loukas Grafakos, Yan Lin +2

The fractional Hilbert transform was introduced by Zayed [30, Zayed, 1998] and has been widely used in signal processing. In view of is connection with the fractional Fourier trans…

cs.LG2021★ 1 cited

ADDS: Adaptive Differentiable Sampling for Robust Multi-Party Learning

Maoguo Gong, Yuan Gao, Yue Wu +1

Distributed multi-party learning provides an effective approach for training a joint model with scattered data under legal and practical constraints. However, due to the quagmire o…

math.NA2021★ 15 cited

Towards fast weak adversarial training to solve high dimensional parabolic partial differential equations using XNODE-WAN

Paul Valsecchi Oliva, Yue Wu, Cuiyu He +1

Due to the curse of dimensionality, solving high dimensional parabolic partial differential equations (PDEs) has been a challenging problem for decades. Recently, a weak adversaria…

cs.LG2021

Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons

Yue Wu, Tao Jin, Hao Lou +3

In heterogeneous rank aggregation problems, users often exhibit various accuracy levels when comparing pairs of items. Thus a uniform querying strategy over users may not be optima…