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Pengcheng Wu

10 papers hereh-index 6252 citations13 works total

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

author position
  • middle author10

Across the 10 of 10 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.AI2
  • cs.CL1
  • cs.CV1
  • cs.NE1
  • cs.SD1
same name
  • Pengcheng Wu — 6 papers, h 14
  • Pengcheng Wu — 6 papers, h 11
  • Pengcheng Wu — 5 papers, h 2
  • Pengcheng Wu — 2 papers, h 1
  • Pengcheng Wu — 2 papers, h 7
  • Pengcheng 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
20242026
most citedTest-Time Model Adaptation with Only Forward Passes

3 citations · 3 across the 9 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2025

ZeroSiam: An Efficient Asymmetry for Test-Time Entropy Optimization without Collapse

Guohao Chen, Shuaicheng Niu, Deyu Chen +5

Test-time entropy minimization helps adapt a model to novel environments and incentivize its reasoning capability, unleashing the model's potential during inference by allowing it…

cs.LG2025

Continual Optimization with Symmetry Teleportation for Multi-Task Learning

Zhipeng Zhou, Ziqiao Meng, Pengcheng Wu +2

Multi-task learning (MTL) is a widely explored paradigm that enables the simultaneous learning of multiple tasks using a single model. Despite numerous solutions, the key issues of…

cs.LG2025

HDT: Hierarchical Discrete Transformer for Multivariate Time Series Forecasting

Shibo Feng, Peilin Zhao, Liu Liu +2

Generative models have gained significant attention in multivariate time series forecasting (MTS), particularly due to their ability to generate high-fidelity samples. Forecasting…

cs.LG2024★ 3 cited

Test-Time Model Adaptation with Only Forward Passes

Shuaicheng Niu, Chunyan Miao, Guohao Chen +2

Test-time adaptation has proven effective in adapting a given trained model to unseen test samples with potential distribution shifts. However, in real-world scenarios, models are…

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