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researcher

R. Thapa

12 papers hereh-index 8249 citations19 works total

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

author position
  • first author6
  • middle author6

Across the 12 of 12 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.CL3
  • cs.CV3
  • cs.AI1
  • cs.MA1
same name
  • R. Thapa — 6 papers, h 7
  • R. Thapa — 5 papers, h 24
  • R. Thapa — 3 papers, h 14
  • R. Thapa — 3 papers, h 3
  • R. Thapa — 2 papers, h 41
  • R. Thapa — 2 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 citedSleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signals

6 citations · 21 across the 12 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

cs.AI2026

Learning transferable human physiology from two million hours of sleep with SleepFM-2

Rahul Thapa, Christopher Sun, William Theodor Lehn-Schioler +14

Sleep provides a nightly window into health by capturing coordinated activity across the brain, heart, muscles and respiratory system. We introduce SleepFM-2, a sleep foundation mo…

cs.MA2026

Agentic AI-enabled discovery across large-scale sleep physiology

Rahul Thapa, Umaer Hanif, Robin Guillard +10

Sleep occupies roughly one-third of human life, yet many aspects of its physiology remain poorly understood. Large polysomnography (PSG) datasets offer new opportunities to study s…

cs.CL2026★ 3 cited

Gemma 4 Technical Report

Gemma Team, Sherif El Abd, Vaibhav Aggarwal +320

We introduce Gemma 4, a new generation of open-weight, natively multimodal language models in the Gemma model family. Designed to advance compute efficiency and reasoning, the Gemm…

cs.LG2026

Structured Scaling of AI Discovery Across Diverse Scientific Domains

Haotian Ye, Haowei Lin, Jingyi Tang +30

Scientific discovery often requires many cycles of proposing, testing, and refining candidate solutions. Language models can increasingly participate in these loops, but simply gen…

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