Spread the love“`html Understanding how to create a neural network can be a game-changer in the fields of artificial intelligence and machine learning. As industries increasingly rely on data-driven ...

Our library allows automatic bound derivation and computation for general computational graphs, in a similar manner that gradients are obtained in modern deep learning frameworks -- users only define ...

PyHGF is a Python library for creating and manipulating dynamic probabilistic networks for predictive coding. These networks approximate Bayesian inference by optimizing beliefs through the diffusion ...

Abstract: Bottleneck structures have been recently introduced as an efficient mathematical framework for modeling communication systems. Leveraging fast computational graph algorithms from the field ...

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Growth-transform (GT) neurons and their population models allow for independent control over the spiking statistics and the transient population dynamics while optimizing a physically plausible ...

Abstract: Recently, several studies have proposed methods to utilize some classes of optimization problems in designing deep neural networks to encode constraints that conventional layers cannot ...