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S. Chopra

14 papers hereh-index 2927.6k citations120 works total

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

author position
  • middle author12
  • last author2

Across the 14 of 14 papers where every author was matched, so the position is known.

fields
  • cs.CV5
  • cs.CL4
  • cs.LG3
  • eess.IV2
same name
  • S. Chopra — 3 papers, h 24
  • S. Chopra — 2 papers, h 30
  • S. Chopra — 1 paper, h 1
  • S. Chopra — 1 paper, h 8

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
20152026
most citedLarge-scale Simple Question Answering with Memory Networks

566 citations · 697 across the 7 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2024

Fine-Tuning In-House Large Language Models to Infer Differential Diagnosis from Radiology Reports

Luoyao Chen, Revant Teotia, Antonio Verdone +4

Radiology reports summarize key findings and differential diagnoses derived from medical imaging examinations. The extraction of differential diagnoses is crucial for downstream ta…

cs.CL2024★ 1 cited

BURExtract-Llama: An LLM for Clinical Concept Extraction in Breast Ultrasound Reports

Yuxuan Chen, Haoyan Yang, Hengkai Pan +6

Breast ultrasound is essential for detecting and diagnosing abnormalities, with radiology reports summarizing key findings like lesion characteristics and malignancy assessments. E…

cs.CL2017★ 92 cited

StarSpace: Embed All The Things!

Ledell Wu, Adam Fisch, Sumit Chopra +3

We present StarSpace, a general-purpose neural embedding model that can solve a wide variety of problems: labeling tasks such as text classification, ranking tasks such as informat…

cs.CL2017★ 8 cited

Training Language Models Using Target-Propagation

Sam Wiseman, Sumit Chopra, Marc'Aurelio Ranzato +4

While Truncated Back-Propagation through Time (BPTT) is the most popular approach to training Recurrent Neural Networks (RNNs), it suffers from being inherently sequential (making…

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