◍wovepaper
SearchResearchersInstitutions
Sign in
researcher

Hau-Tieng Wu

16 papers hereh-index 342 citations22 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author1
  • first author1
  • middle author4
  • last author10

Across the 16 of 16 papers where every author was matched, so the position is known.

fields
  • math.ST4
  • eess.SP3
  • cs.LG2
  • stat.ME2
  • stat.ML2
  • math.CA1
same name
  • Hau-Tieng Wu — 97 papers, h 39
  • Hau-Tieng Wu — 4 papers, h 1
  • Hau-Tieng Wu — 3 papers, h 5
  • Hau-Tieng Wu — 2 papers, h 3
  • Hau-Tieng Wu — 1 paper, h 4
  • Hau-Tieng Wu — 1 paper, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232026
most citedRidge detection for nonstationary multicomponent signals with time-varying wave-shape functions and its applications

1 citations · 2 across the 15 of their papers we have counts for

collaborators
Showing 2024Show all

4 papers · 1 filter

cs.LG2024

Sleep Staging from Airflow Signals Using Fourier Approximations of Persistence Curves

Shashank Manjunath, Hau-Tieng Wu, Aarti Sathyanarayana

Sleep staging is a challenging task, typically manually performed by sleep technologists based on electroencephalogram and other biosignals of patients taken during overnight sleep…

cs.LG2024

Landmark Alternating Diffusion

Sing-Yuan Yeh, Hau-Tieng Wu, Ronen Talmon +1

Alternating Diffusion (AD) is a commonly applied diffusion-based sensor fusion algorithm. While it has been successfully applied to various problems, its computational burden remai…

stat.ML2024★ 1 cited

Convergence analysis of t-SNE as a gradient flow for point cloud on a manifold

Seonghyeon Jeong, Hau-Tieng Wu

We present a theoretical foundation regarding the boundedness of the t-SNE algorithm. t-SNE employs gradient descent iteration with Kullback-Leibler (KL) divergence as the objectiv…

stat.ML2024

Design a Metric Robust to Complicated High Dimensional Noise for Efficient Manifold Denoising

Hau-Tieng Wu

In this manuscript, we propose an efficient manifold denoiser based on landmark diffusion and optimal shrinkage under the complicated high dimensional noise and compact manifold se…

◍wovepaper

Papers, researchers and institutions, woven together.

Explore
  • Search
  • Researchers
  • Institutions
Account
  • Library
  • Chat
Data
  • arXiv.org
  • Semantic Scholar
  • OpenAlex
  • Latest RSS
AboutContactPrivacyDevelopersllms.txtopenapi.json
Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.