activity
20162024
most citedA Compositional Object-Based Approach to Learning Physical Dynamics

166 citations · 326 across the 9 of their papers we have counts for

collaborators

9 papers

q-bio.NC2024★ 36 cited

Object permanence in newborn chicks is robust against opposing evidence

Justin N. Wood, Tomer D. Ullman, Brian W. Wood +2

Newborn animals have advanced perceptual skills at birth, but the nature of this initial knowledge is unknown. Is initial knowledge flexible, continuously adapting to the statistic…

cs.AI2024

MMToM-QA: Multimodal Theory of Mind Question Answering

Chuanyang Jin, Yutong Wu, Jing Cao +7

Theory of Mind (ToM), the ability to understand people's mental states, is an essential ingredient for developing machines with human-level social intelligence. Recent machine lear…

cs.AI2023

In-Context Learning Dynamics with Random Binary Sequences

Eric J. Bigelow, Ekdeep Singh Lubana, Robert P. Dick +2

Large language models (LLMs) trained on huge corpora of text datasets demonstrate intriguing capabilities, achieving state-of-the-art performance on tasks they were not explicitly…

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.AI2022

Type theory in human-like learning and inference

Felix A. Sosa, Tomer Ullman

Humans can generate reasonable answers to novel queries (Schulz, 2012): if I asked you what kind of food you want to eat for lunch, you would respond with a food, not a time. The t…

cs.CV2022★ 40 cited

Testing Relational Understanding in Text-Guided Image Generation

Colin Conwell, Tomer Ullman

Relations are basic building blocks of human cognition. Classic and recent work suggests that many relations are early developing, and quickly perceived. Machine models that aspire…

cs.LG2021

Temporal and Object Quantification Networks

Jiayuan Mao, Zhezheng Luo, Chuang Gan +4

We present Temporal and Object Quantification Networks (TOQ-Nets), a new class of neuro-symbolic networks with a structural bias that enables them to learn to recognize complex rel…

cs.AI2021★ 1 cited

AGENT: A Benchmark for Core Psychological Reasoning

Tianmin Shu, Abhishek Bhandwaldar, Chuang Gan +6

For machine agents to successfully interact with humans in real-world settings, they will need to develop an understanding of human mental life. Intuitive psychology, the ability t…

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…