◍wovepaper
SearchResearchersInstitutions
Sign in
researcher

N. Subramanyam

4 papers here

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

author position
  • last author4

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

fields
  • cs.LG3
  • cs.CV1

identity via Semantic Scholar / OpenAlex

most citedSurveillance of COVID-19 Pandemic using Hidden Markov Model

7 citations · 15 across the 4 of their papers we have counts for

collaborators

4 papers

cs.LG2021★ 7 cited

Robustness to Augmentations as a Generalization metric

Sumukh Aithal K, Dhruva Kashyap, Natarajan Subramanyam

Generalization is the ability of a model to predict on unseen domains and is a fundamental task in machine learning. Several generalization bounds, both theoretical and empirical h…

cs.CV2020

Graph Based Temporal Aggregation for Video Retrieval

Arvind Srinivasan, Aprameya Bharadwaj, Aveek Saha +1

Large scale video retrieval is a field of study with a lot of ongoing research. Most of the work in the field is on video retrieval through text queries using techniques such as VS…

cs.LG2020★ 7 cited

Surveillance of COVID-19 Pandemic using Hidden Markov Model

Shreekanth M. Prabhu, Natarajan Subramaniam

COVID-19 pandemic has brought the whole world to a stand-still over the last few months. In particular the pace at which pandemic has spread has taken everybody off-guard. The Gove…

cs.LG2020★ 1 cited

Transfer Learning using Neural Ordinary Differential Equations

Rajath S, Sumukh Aithal K, Natarajan Subramanyam

A concept of using Neural Ordinary Differential Equations(NODE) for Transfer Learning has been introduced. In this paper we use the EfficientNets to explore transfer learning on CI…

◍wovepaper

Papers, researchers and institutions, woven together.

Explore
  • Search
  • Researchers
  • Institutions
Account
  • Library
  • Chat
Data
  • arXiv.org
  • Semantic Scholar
  • OpenAlex
  • Latest RSS
AboutContactPrivacyDevelopersllms.txtopenapi.json
Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.