Traditional statistical and machine learning methods mostly focus on correlations, but causal models allow researchers to infer mechanisms and predict the effects of interventions. Nevertheless, the ...

This research presents a Driver Drowsiness Detection System (DDDS) that uses a Convolutional Neural Network (CNN) to improve road safety. The system uses a vast dataset of 97,860 images from the ...

This repository contains a PyTorch implementation of Salesforce Research's Quasi-Recurrent Neural Networks paper. The QRNN provides similar accuracy to the LSTM but can be betwen 2 and 17 times faster ...

Training neural networks to perform different tasks is relevant across various disciplines. In particular, Recurrent Neural Networks (RNNs) are of great interest in Computational Neuroscience.

Machine learning is a complex discipline but implementing machine learning models is far less daunting than it used to be. Machine learning frameworks like Google’s TensorFlow ease the process of ...

This publication provides an in-depth overview of various neural network layers, including their historical development, mathematical formulations, and code implementations. We cover common layer ...

This important work provides evidence that artificial recurrent neural networks can be used to investigate neural mechanisms underlying reversible remapping of spatial representations. Authors perform ...