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Tomer Ullman

3 papers here

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

author position
  • sole author1
  • middle author1
  • last author1

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

fields
  • cs.AI2
  • cs.CV1
ORCID 0000-0003-1722-2382

identity via Semantic Scholar / OpenAlex

activity
20162023
most citedA Compositional Object-Based Approach to Learning Physical Dynamics

166 citations · 289 across the 3 of their papers we have counts for

collaborators
Showing cs.AIShow all

2 papers · 1 filter

cs.AI2023★ 83 cited

Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks

Tomer Ullman

Intuitive psychology is a pillar of common-sense reasoning. The replication of this reasoning in machine intelligence is an important stepping-stone on the way to human-like artifi…

cs.AI2016★ 166 cited

A Compositional Object-Based Approach to Learning Physical Dynamics

Michael B. Chang, Tomer Ullman, Antonio Torralba +1

We present the Neural Physics Engine (NPE), a framework for learning simulators of intuitive physics that naturally generalize across variable object count and different scene conf…

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