◍wovepaper
SearchResearchersInstitutions
Sign in
researcher

Anirudh Goyal

Mila, University of Montreal

47 papers hereh-index 367.6k citations92 works total

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

author position
  • first author8
  • middle author35
  • last author2

Across the 45 of 47 papers where every author was matched, so the position is known.

fields
  • cs.LG22
  • stat.ML9
  • cs.AI8
  • cs.CV4
  • cs.CL3
  • q-bio.MN1
affiliations
  • Mila, University of Montreal
Homepage
same name
  • Anirudh Goyal — 19 papers, h 10
  • Anirudh Goyal — 13 papers, h 14
  • Anirudh Goyal — 13 papers, h 6
  • Anirudh Goyal — 4 papers
  • Anirudh Goyal — 4 papers, h 4

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
20172024
most citedA Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms

122 citations · 466 across the 39 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2024

Zero-Shot Object-Centric Representation Learning

Aniket Didolkar, Andrii Zadaianchuk, Anirudh Goyal +4

The goal of object-centric representation learning is to decompose visual scenes into a structured representation that isolates the entities. Recent successes have shown that objec…

cs.CV2023

Cycle Consistency Driven Object Discovery

Aniket Didolkar, Anirudh Goyal, Yoshua Bengio

Developing deep learning models that effectively learn object-centric representations, akin to human cognition, remains a challenging task. Existing approaches facilitate object di…

cs.CV2023

Spotlight Attention: Robust Object-Centric Learning With a Spatial Locality Prior

Ayush Chakravarthy, Trang Nguyen, Anirudh Goyal +2

The aim of object-centric vision is to construct an explicit representation of the objects in a scene. This representation is obtained via a set of interchangeable modules called \…

cs.CV2023

Leveraging the Third Dimension in Contrastive Learning

Sumukh Aithal, Anirudh Goyal, Alex Lamb +2

Self-Supervised Learning (SSL) methods operate on unlabeled data to learn robust representations useful for downstream tasks. Most SSL methods rely on augmentations obtained by tra…

◍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.