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researcher

D. Sarkar

4 papers hereh-index 15773 citations89 works total

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

author position
  • first author2
  • middle author1
  • last author1

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

fields
  • cs.LG2
  • cs.CV1
  • cs.LO1
same name
  • D. Sarkar — 186 papers
  • D. Sarkar — 63 papers, h 14
  • D. Sarkar — 28 papers, h 12
  • D. Sarkar — 16 papers, h 60
  • D. Sarkar — 9 papers, h 9
  • D. Sarkar — 6 papers, h 3

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
20102021
most citedFed-Focal Loss for imbalanced data classification in Federated Learning

34 citations · 34 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CV2021

One Shot Audio to Animated Video Generation

Neeraj Kumar, Srishti Goel, Ankur Narang +4

We consider the challenging problem of audio to animated video generation. We propose a novel method OneShotAu2AV to generate an animated video of arbitrary length using an audio c…

cs.LG2020

CatFedAvg: Optimising Communication-efficiency and Classification Accuracy in Federated Learning

Dipankar Sarkar, Sumit Rai, Ankur Narang

Federated learning has allowed the training of statistical models over remote devices without the transfer of raw client data. In practice, training in heterogeneous and large netw…

cs.LG2020★ 34 cited

Fed-Focal Loss for imbalanced data classification in Federated Learning

Dipankar Sarkar, Ankur Narang, Sumit Rai

The Federated Learning setting has a central server coordinating the training of a model on a network of devices. One of the challenges is variable training performance when the da…

cs.LO2010

Equivalence Checking in Embedded Systems Design Verification

S. Bandyopadhyay, D. Sarkar, C. R. Mandal

In this report we focus on some aspects related to modeling and formal verification of embedded systems. Many models have been proposed to represent embedded systems. These models…

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