Quantization-Aware Training What is knowledge distillation and how can it be used to compress neural networks

Feb 6, 2025 ... Abstract and Figures. While there is much knowledge about local neuronal circuitry, considerably less is known about how neuronal input is ... Feb 24, 2020 ... We show how to encode both types of domain knowledge into the widely used Gaussian scale mixture priors with Automatic Relevance Determination. 01/01/2024 Maryam Shanechi is elevated to IEEE Fellow. 12/11/2023 Our Nature Biomedical Engineering paper on neural network models with dynamical flexible ... Sat 7:10 a.m. - 7:18 a.m.. Neural networks learn an environment's geometry in latent space by performing predictive coding on visual scenes ( Oral ) > link a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process. Informational capacity and recall quality in sparsely encoded Hopfield-like neural network: analytical approaches and computer simulation Jan 16, 2021 ... ... informational and non-informational classes, respectively ... The deep neural network (DNN) classifier obtained the highest accuracy (95.2%). We propose a novel neural network architecture which trains spatial positions of neural soma and axon pairs, where weights are calculated by axon-soma ... The informational aspects of neural networks performing associative memory following Hebbian learning are analysed in detail. The recall process is decomposed ... Neural networks can realize conditional distributions of the form p(y. z I x. u) specified by network parameter u. The set of such realizable distributions is a ...