4 papers
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…
Learning Rational Subgoals from Demonstrations and Instructions
Zhezheng Luo, Jiayuan Mao, Jiajun Wu +3
We present a framework for learning useful subgoals that support efficient long-term planning to achieve novel goals. At the core of our framework is a collection of rational subgo…
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…
Edge Matching with Inequalities, Triangles, Unknown Shape, and Two Players
Jeffrey Bosboom, Charlotte Chen, Lily Chung +12
We analyze the computational complexity of several new variants of edge-matching puzzles. First we analyze inequality (instead of equality) constraints between adjacent tiles, prov…