I’ve been trying to apply Sparse Distributed Representations (SDR) to simple neural network layers. Can anyone suggest a method for achieving this? I want to ensure that only 2-5% of the neurons are active in each layer

A neural vocoder is a neural network designed to generate speech waveforms given acoustic features — often used as a backbone module for speech recognition tasks such as text-to-speech (TTS), speech-to-speech translation (S2ST), etc. Current neural vocoders however can struggle to maintain high sound quality without incurring high computational costs. In the new paper WaveFit

# Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights ## Abstract Probabilistic neural networks are typically modeled with independent weight priors, which do not capture weight correlations in the prior and do not provide a parsimonious interface to express properties in function space. A desirable class of priors would represent weights compactly, capture correlations between weights, facilitate calibrated reasoning about uncertainty, and allow inclusion of prior knowledge about the

Explore how synthetic data generation enhances neural network training by providing diverse, scalable datasets that improve model accuracy and robustness withou

This paper introduces a framework using line graph transformations to recast link prediction as node classification, boosting accuracy and efficiency.

Last week I trained a neural net on 1000 candles, and soon it was producing scents like Frozen Styrofoam, Volcanoes Comfort, Lemon Lime Decay, and Friendly Wetsuit. We have to just imagine what these would smell like (or in some cases, try not to imagine). But what if the neural net could describe them

I haven't blogged these days , It's mainly about watching matlab Neural network based on Neural Network , Through the learning of machine learning

何回かの講演スライドをNeural Fieldの紹介としてまとめ直しました． 誤りなどあればお知らせください．

Edward Github Mixture Density Networks Mixture density networks (MDN) (Bishop, 1994) are a class of models obtained by combining a conventional neural network with a mixture density model. We demonstrate with an example in Edward. An interactive version with Jupyter notebook is available here . Data We use the same toy data from David Ha’s blog post , where he explains MDNs. It is an inverse problem where for every input \(x_n\) there are multiple outputs \(y_n\). from sklearn.model_selection import train

Hierarchical Reasoning Model (HRM) is a novel approach using two small neural networks recursing at different frequencies. This biologically inspired method beats Large