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Junhong Shen

6 papers hereh-index 7327 citations9 works total

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

author position
  • first author3
  • middle author3

Across the 6 of 6 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.CV1
  • cs.SE1
same name
  • Junhong Shen — 3 papers, h 5
  • Junhong Shen — 2 papers, h 2
  • Junhong Shen — 2 papers, h 2
  • Junhong Shen — 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 citedUPS: Efficiently Building Foundation Models for PDE Solving via Cross-Modal Adaptation

5 citations · 10 across the 6 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2025

Thinking vs. Doing: Agents that Reason by Scaling Test-Time Interaction

Junhong Shen, Hao Bai, Lunjun Zhang +8

The current paradigm of test-time scaling relies on generating long reasoning traces ("thinking" more) before producing a response. In agent problems that require interaction, this…

cs.LG2025

CodePDE: An Inference Framework for LLM-driven PDE Solver Generation

Shanda Li, Tanya Marwah, Junhong Shen +4

Partial differential equations (PDEs) are fundamental to modeling physical systems, yet solving them remains a complex challenge. Traditional numerical solvers rely on expert knowl…

cs.LG2024★ 5 cited

Specialized Foundation Models Struggle to Beat Supervised Baselines

Zongzhe Xu, Ritvik Gupta, Wenduo Cheng +4

Following its success for vision and text, the "foundation model" (FM) paradigm -- pretraining large models on massive data, then fine-tuning on target tasks -- has rapidly expande…

cs.LG2024

UPS: Efficiently Building Foundation Models for PDE Solving via Cross-Modal Adaptation

Junhong Shen, Tanya Marwah, Ameet Talwalkar

We present Unified PDE Solvers (UPS), a data- and compute-efficient approach to developing unified neural operators for diverse families of spatiotemporal PDEs from various domains…

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