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Yuhan Tang

4 papers here

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

author position
  • sole author1
  • middle author2

Across the 3 of 4 papers where every author was matched, so the position is known.

fields
  • cs.AI1
  • cs.CY1
  • cs.LG1
  • stat.AP1
same name
  • Yuhan Tang — 3 papers
  • Yuhan Tang — 1 paper, h 3
  • Yuhan Tang — 1 paper
  • Yuhan Tang — 1 paper, h 1
  • Yuhan Tang — 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

most citedMultilevel Interpretability Of Artificial Neural Networks: Leveraging Framework And Methods From Neuroscience

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

collaborators

4 papers

cs.LG2025

RAST-MoE-RL: A Regime-Aware Spatio-Temporal MoE Framework for Deep Reinforcement Learning in Ride-Hailing

Yuhan Tang, Kangxin Cui, Jung Ho Park +6

Ride-hailing platforms face the challenge of balancing passenger waiting times with overall system efficiency under highly uncertain supply-demand conditions. Adaptive delayed matc…

stat.AP2025

Diabetes Lifestyle Medicine Treatment Assistance Using Reinforcement Learning

Yuhan Tang

Type 2 diabetes prevention and treatment can benefit from personalized lifestyle prescriptions. However, the delivery of personalized lifestyle medicine prescriptions is limited by…

cs.CY2025

Simulating Society Requires Simulating Thought

Chance Jiajie Li, Jiayi Wu, Zhenze Mo +10

Simulating society with large language models (LLMs), we argue, requires more than generating plausible behavior; it demands cognitively grounded reasoning that is structured, revi…

cs.AI2024★ 1 cited

Multilevel Interpretability Of Artificial Neural Networks: Leveraging Framework And Methods From Neuroscience

Zhonghao He, Jascha Achterberg, Katie Collins +13

As deep learning systems are scaled up to many billions of parameters, relating their internal structure to external behaviors becomes very challenging. Although daunting, this pro…

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