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T. Dam

3 papers here

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

author position
  • first author1
  • middle author1
  • last author1

Across the 3 of 3 papers where every author was matched, so the position is known.

fields
  • cs.LG2
  • eess.IV1
same name
  • T. Dam — 1 paper
  • T. Dam — 1 paper
  • T. Dam — 1 paper

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

most citedDoes Adversarial Oversampling Help us?

1 citations · 1 across the 3 of their papers we have counts for

collaborators

3 papers

eess.IV2022

Improving Self-supervised Learning for Out-of-distribution Task via Auxiliary Classifier

Harshita Boonlia, Tanmoy Dam, Md Meftahul Ferdaus +2

In real world scenarios, out-of-distribution (OOD) datasets may have a large distributional shift from training datasets. This phenomena generally occurs when a trained classifier…

cs.LG2021

Rainfall-runoff prediction using a Gustafson-Kessel clustering based Takagi-Sugeno Fuzzy model

Subhrasankha Dey, Tanmoy Dam

A rainfall-runoff model predicts surface runoff either using a physically-based approach or using a systems-based approach. Takagi-Sugeno (TS) Fuzzy models are systems-based approa…

cs.LG2021★ 1 cited

Does Adversarial Oversampling Help us?

Tanmoy Dam, Md Meftahul Ferdaus, Sreenatha G. Anavatti +2

Traditional oversampling methods are generally employed to handle class imbalance in datasets. This oversampling approach is independent of the classifier; thus, it does not offer…

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