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On the Expressiveness and Generalization of Hypergraph Neural Networks
Next, we analyze the learning properties of these neural networks, especially focusing on how they can be trained on a finite set of small graphs and generalize to larger graphs, which we term structural generalization.
Learning Rational Subgoals from Demonstrations and Instructions
We present a framework for learning useful subgoals that support efficient long-term planning to achieve novel goals.
On the Expressiveness and Learning of Relational Neural Networks on Hypergraphs
Our first contribution is a fine-grained analysis of the expressiveness of these neural networks, that is, the set of functions that they can realize and the set of problems that they can solve.
Learning Rational Skills for Planning from Demonstrations and Instructions
We present a framework for learning compositional, rational skill models (RatSkills) that support efficient planning and inverse planning for achieving novel goals and recognizing activities.
Temporal and Object Quantification Networks
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 relational-temporal events.
Temporal and Object Quantification Nets
We aim to learn generalizable representations for complex activities by quantifying over both entities and time, as in “the kicker is behind all the other players,” or “the player controls the ball until it moves toward the goal.” Such a structural inductive bias of object relations, object quantification, and temporal orders will enable the learned representation to generalize to situations with varying numbers of agents, objects, and time courses.
