Jul 8, 2020 � Combining neural networks with continuous logic and multicriteria decision-making tools can reduce the black-box nature of neural models.
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Oct 21, 2020 � Neural networks are able to learn any function. But learning a highly non-linear function such as the modulo operation is certainly not trivial.
These results imply a classification theory describing conditions under which (stable) NNs with a given accuracy can be computed by an algorithm. We begin this�...
We propose a general framework capable of enhancing various types of neural networks (e.g., CNNs and. RNNs) with declarative first-order logic rules.
Feb 7, 2023 � What if we could do away with all the complexities of a neuron and just model neural networks ...
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Posted: Feb 7, 2023
Duration: 1:34:53
Posted: Feb 7, 2023
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Oct 1, 2019 � I'm interested in learning about Neural Networks and implementing them. I'm particularly interested in GANs and LSTM networks. I understand�...
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A pLogicNet defines the joint distribution of all possible triplets by using a Markov logic network with first-order logic, which can be efficiently optimized�...
Since traditional neural networks and second-order counterparts can represent each other and fuzzy logic operations are naturally implemented in second- order�...
Dec 23, 2021 � We propose a variational deep logic network that incorporates both representation learning and relational reasoning via the variational EM algorithm.
Neural Three-valued-logic Networks. S.C.Chan, L.S. "I,. S.Brody andH.H.Teh. National University of Singapore. Singapore 051 1. Republic of Singapore. ABSTRACT.