Jan 25, 2022 · This paper introduces a framework that unifies both approaches. We propose a loss, neuro-symbolic entropy regularization, that encourages the ...

One approach – entropy regularization – posits that decision boundaries should lie in low- probability regions. It extracts supervision from unlabeled examples, ...

In structured output prediction, the goal is to jointly predict several output variables that together en- code a structured object – a path in a graph, an.

This paper introduces a framework that unifies both approaches. We propose a loss, neuro-symbolic entropy regularization, that encourages the model to ...

This paper proposes a loss, neuro-symbolic entropy regularization, that encourages the model to confidently predict a valid object by restricting ...

Neuro-Symbolic Entropy Regularization (Supplementary material). Kareem Ahmed1 ... the coefficient of the entropy loss in the range [1 × 100, 1 ×. 10−1 ...

Jan 25, 2022 · Entropy regularization steers the network towards confident, possibly invalid predictions (b). Neuro-symbolic learning steers the network ...

Jan 22, 2024 · This framework, called CCN+, integrates the requirements into the output layer of the neural network by applying multiple inference rules that ...

Duration: 2:14
Posted: Sep 1, 2022