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Arindam Ghosh

3M/Solventum Healthcare

4 papers hereh-index 4156 citations5 works total

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

author position
  • first author3
  • middle author1

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

fields
  • eess.AS2
  • cs.CL1
  • cs.LG1
affiliations
  • 3M/Solventum Healthcare
Homepage
same name
  • Arindam Ghosh — 5 papers, h 6
  • Arindam Ghosh — 4 papers, h 2
  • Arindam Ghosh — 3 papers
  • Arindam Ghosh — 3 papers, h 9
  • Arindam Ghosh — 3 papers, h 3
  • Arindam Ghosh — 2 papers, h 0

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
20212025
most citedAdaFocal: Calibration-aware Adaptive Focal Loss

11 citations · 13 across the 4 of their papers we have counts for

collaborators

4 papers

eess.AS2025

ASR-Synchronized Speaker-Role Diarization

Arindam Ghosh, Mark Fuhs, Bongjun Kim +2

Speaker-role diarization (RD), such as doctor vs. patient or lawyer vs. client, is practically often more useful than conventional speaker diarization (SD), which assigns only gene…

cs.LG2022★ 11 cited

AdaFocal: Calibration-aware Adaptive Focal Loss

Arindam Ghosh, Thomas Schaaf, Matthew R. Gormley

Much recent work has been devoted to the problem of ensuring that a neural network's confidence scores match the true probability of being correct, i.e. the calibration problem. Of…

eess.AS2022★ 1 cited

Low-resource Low-footprint Wake-word Detection using Knowledge Distillation

Arindam Ghosh, Mark Fuhs, Deblin Bagchi +2

As virtual assistants have become more diverse and specialized, so has the demand for application or brand-specific wake words. However, the wake-word-specific datasets typically u…

cs.CL2021★ 1 cited

Leveraging Pretrained Models for Automatic Summarization of Doctor-Patient Conversations

Longxiang Zhang, Renato Negrinho, Arindam Ghosh +4

Fine-tuning pretrained models for automatically summarizing doctor-patient conversation transcripts presents many challenges: limited training data, significant domain shift, long…

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