These efforts are promising but limited. AI minds don't necessarily map to human concepts. Some believe that we need to ...

This paper introduces a new generative deep learning network for human motion synthesis and control. Our key idea is to combine recurrent neural networks (RNNs) and adversarial training for human ...

Recent studies have demonstrated the effectiveness of supervised learning in spiking neural networks (SNNs). A trainable SNN provides a valuable tool not only for engineering applications but also for ...

A team of scientists in the United States has combined both spatial and temporal attention mechanisms to develop a new approach for PV inverter fault detection. Training the new method on a dataset ...

Deep neural networks (DNNs) are a class of artificial neural networks (ANNs) that are deep in the sense that they have many layers of hidden units between the input and output layers. Deep neural ...

This example shows how to build and train a convolutional neural network (CNN) from scratch to perform a classification task with an EEG dataset.