Sander Bohte | DeepAI

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∙ 04/20/2023

Efficient Uncertainty Estimation in Spiking Neural Networks via MC-dropout

Spiking neural networks (SNNs) have gained attention as models of sparse...

0 Tao Sun, et al. ∙

∙ 07/23/2022

A Taxonomy of Recurrent Learning Rules

Backpropagation through time (BPTT) is the de facto standard for trainin...

0 Guillermo Martín-Sánchez, et al. ∙

∙ 11/24/2021

Markov Chain Generative Adversarial Neural Networks for Solving Bayesian Inverse Problems in Physics Applications

In the context of solving inverse problems for physics applications with...

0 Nikolaj T. Mücke, et al. ∙

∙ 11/05/2018

A Biologically Plausible Learning Rule for Deep Learning in the Brain

Researchers have proposed that deep learning, which is providing importa...

30 Isabella Pozzi, et al. ∙

∙ 06/13/2018

An image representation based convolutional network for DNA classification

The folding structure of the DNA molecule combined with helper molecules...

0 Bojian Yin, et al. ∙

∙ 10/13/2017

Efficient Computation in Adaptive Artificial Spiking Neural Networks

Artificial Neural Networks (ANNs) are bio-inspired models of neural comp...

0 Davide Zambrano, et al. ∙

∙ 03/14/2017

Conditional Time Series Forecasting with Convolutional Neural Networks

We present a method for conditional time series forecasting based on the...

0 Anastasia Borovykh, et al. ∙

∙ 03/11/2016

Efficient forward propagation of time-sequences in convolutional neural networks using Deep Shifting

When a Convolutional Neural Network is used for on-the-fly evaluation of...

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