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Yu-Hao Huang

Nanjing University

7 papers hereh-index 6120 citations7 works total

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

author position
  • first author2
  • middle author5

Across the 7 of 7 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.CE2
  • cs.CV1
affiliations
  • Nanjing University
ORCID 0009-0004-0140-8267
same name
  • Yu-Hao Huang — 5 papers, h 8
  • Yu-Hao Huang — 2 papers, h 3
  • Yu-Hao Huang — 1 paper, h 8
  • Yu-Hao Huang — 1 paper, h 3

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

most citedNumLLM: Numeric-Sensitive Large Language Model for Chinese Finance

1 citations · 1 across the 6 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2025

MIRA: Medical Time Series Foundation Model for Real-World Health Data

Hao Li, Bowen Deng, Chang Xu +8

A unified foundation model for medical time series -- pretrained on open access and ethics board-approved medical corpora -- offers the potential to reduce annotation burdens, mini…

cs.LG2025

BRIDGE: Bootstrapping Text to Control Time-Series Generation via Multi-Agent Iterative Optimization and Diffusion Modeling

Hao Li, Yu-Hao Huang, Chang Xu +5

Time-series Generation (TSG) is a prominent research area with broad applications in simulations, data augmentation, and counterfactual analysis. While existing methods have shown…

cs.LG2025

TimeDP: Learning to Generate Multi-Domain Time Series with Domain Prompts

Yu-Hao Huang, Chang Xu, Yueying Wu +2

Time series generation models are crucial for applications like data augmentation and privacy preservation. Most existing time series generation models are typically designed to ge…

cs.LG2024

MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process

Xinyao Fan, Yueying Wu, Chang Xu +3

Recently, diffusion probabilistic models have attracted attention in generative time series forecasting due to their remarkable capacity to generate high-fidelity samples. However,…

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