143 citations · 440 across the 42 of their papers we have counts for
6 papers · 1 filter
What Planning Problems Can A Relational Neural Network Solve?
Jiayuan Mao, Tomás Lozano-Pérez, Joshua B. Tenenbaum +1
Goal-conditioned policies are generally understood to be "feed-forward" circuits, in the form of neural networks that map from the current state and the goal specification to the n…
Sparse and Local Networks for Hypergraph Reasoning
Guangxuan Xiao, Leslie Pack Kaelbling, Jiajun Wu +1
Reasoning about the relationships between entities from input facts (e.g., whether Ari is a grandparent of Charlie) generally requires explicit consideration of other entities that…
On the Expressiveness and Generalization of Hypergraph Neural Networks
Zhezheng Luo, Jiayuan Mao, Joshua B. Tenenbaum +1
This extended abstract describes a framework for analyzing the expressiveness, learning, and (structural) generalization of hypergraph neural networks (HyperGNNs). Specifically, we…
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…
Language-Mediated, Object-Centric Representation Learning
Ruocheng Wang, Jiayuan Mao, Samuel J. Gershman +1
We present Language-mediated, Object-centric Representation Learning (LORL), a paradigm for learning disentangled, object-centric scene representations from vision and language. LO…
Neurally-Guided Structure Inference
Sidi Lu, Jiayuan Mao, Joshua B. Tenenbaum +1
Most structure inference methods either rely on exhaustive search or are purely data-driven. Exhaustive search robustly infers the structure of arbitrarily complex data, but it is…