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researcher

A. Thakur

11 papers hereh-index 9203 citations34 works total

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

author position
  • first author2
  • middle author7
  • last author1

Across the 10 of 11 papers where every author was matched, so the position is known.

fields
  • cs.LG6
  • cs.CL4
  • cs.CV1
same name
  • A. Thakur — 10 papers, h 6
  • A. Thakur — 8 papers, h 12
  • A. Thakur — 5 papers, h 5
  • A. Thakur — 3 papers, h 2
  • A. Thakur — 3 papers, h 2
  • A. Thakur — 3 papers, 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 citedLarge Language Models in the Clinic: A Comprehensive Benchmark

7 citations · 9 across the 11 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

cs.LG2026

Extending Pretrained 10-Second ECG Foundation Models to Longer Horizons

Wei Tang, Jinpei Han, Kangning Cui +10

Electrocardiogram (ECG) foundation models pretrained on typical diagnostic 10-second ECG segments, have demonstrated strong transferability across a range of clinical applications.…

cs.CL2026

BAS: A Decision-Theoretic Approach to Evaluating Large Language Model Confidence

Sean Wu, Fredrik K. Gustafsson, Edward Phillips +3

Large language models (LLMs) often produce confident but incorrect answers in settings where abstention would be safer. Standard evaluation protocols, however, require a response a…

cs.CL2026

Entropy Alone is Insufficient for Safe Selective Prediction in LLMs

Edward Phillips, Fredrik K. Gustafsson, Sean Wu +2

Selective prediction systems can mitigate harms resulting from language model hallucinations by abstaining from answering in high-risk cases. Uncertainty quantification techniques…

cs.LG2026

Democratising Clinical AI through Dataset Condensation for Classical Clinical Models

Anshul Thakur, Soheila Molaei, Pafue Christy Nganjimi +5

Dataset condensation (DC) learns a compact synthetic dataset that enables models to match the performance of full-data training, prioritising utility over distributional fidelity.…

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