Training algorithm breaks barriers to deep physical neural networks Date: December 7, 2023 Source: Ecole Polytechnique Fédérale de Lausanne Summary: Researchers have developed an algorithm to ...

Aware Fine-Tuning of Spiking Q-Networks on the SpiNNaker2 Neuromorphic Platform” was published by researchers at TU Dresden, ScaDS.AI and Centre for Tactile Internet with Human-in-the-Loop (CeTI).

Z Advanced Computing, Inc. (ZAC), the pioneer Cognitive Explainable-AI (Artificial Intelligence) (Cognitive XAI or CXAI) ...

Using this information, the model can then tell us the probability of a drug-protein interaction that we did not previously have in the database, as the algorithms can efficiently analyse large ...

It’s been ten years since AlexNet, a deep learning convolutional neural network (CNN) model running on GPUs, displaced more traditional vision processing algorithms to win the ImageNet Large Scale ...

Researchers are training neural networks to make decisions more like humans would. This science of human decision-making is only just being applied to machine learning, but developing a neural ...

Effectively, the surrogate gradient method was able to correct for the chip’s imperfections during training on the computer. First, the spiking neural network performs a simple task using the varying ...