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researcher

P. Liang

8 papers hereh-index 454 citations11 works total

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

author position
  • sole author1
  • middle author3
  • last author4

Across the 8 of 8 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.AI2
  • cs.ET1
  • cs.HC1
same name
  • P. Liang — 73 papers, h 42
  • P. Liang — 14 papers, h 3
  • P. Liang — 12 papers, h 4
  • P. Liang — 11 papers, h 4
  • P. Liang — 7 papers, h 11
  • P. Liang — 6 papers, h 4

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 citedSmell with Genji: Rediscovering Human Perception through an Olfactory Game with AI

1 citations · 1 across the 8 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2026

A Vision for Multisensory Intelligence: Sensing, Science, and Synergy

Paul Pu Liang

Our experience of the world is multisensory, spanning a synthesis of language, sight, sound, touch, taste, and smell. Yet, artificial intelligence has primarily advanced in digital…

cs.LG2025

EQPO: Equitable Group Relative Policy Optimization for Clinical Reasoning

Shiqi Dai, Wei Dai, Jiaee Cheong +1

Medical AI systems demonstrated impressive diagnostic performance, yet they routinely show uneven accuracy across demographic groups, disadvantaging underrepresented populations. A…

cs.LG2025

FairSSL: Fair Multimodal Self-Supervised Learning

Jiaee Cheong, Abtin Mogharabin, Paul Liang +2

Prevalent multimodal self-supervised learning (SSL) methods rely on the redundancy assumption: that different views share substantial task-relevant information. We argue that this…

cs.LG2024

Balancing Multimodal Training Through Game-Theoretic Regularization

Konstantinos Kontras, Thomas Strypsteen, Christos Chatzichristos +3

Multimodal learning holds promise for richer information extraction by capturing dependencies across data sources. Yet, current training methods often underperform due to modality…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.