When designing a robot, such as Boston Dynamics' anthropomorphic robot Atlas, which appears exercising and sorting boxes, ...

Researchers developed a PV-RNN model that learns like children, integrating language and action to uncover mechanisms of ...

A study by the University of Bonn demonstrates a novel training method for spiking neural networks, which are ...

AI applications like ChatGPT are based on artificial neural networks that, in many respects, imitate the nerve cells in our brains. They are trained with vast quantities of data on high-performance ...

This article establishes a neural network-based technique for automatic peak picking in 2D NMR spectroscopy, demonstrating a ...

Researchers at Oxford University uncover how deep neural networks possess an intrinsic simplicity bias, enabling them to ...

Hypernetworks have gained attention for their ability to efficiently adapt large models or train generative models of neural representations. Despite their effectiveness, training hyper networks are ...

Researchers have designed a CNN model that analyzes vibration data for precise detection of beam damage, highlighting advancements in structural health monitoring.

and hence 20 bits per neuron per image. It is hard to compare this number to efficient deep neural networks, but this number can serve as a starting point for comparing SNN architecture. The actual ...

In the last decade, deep learning models start getting more attention, especially within the image processing community. In this study, we propose a novel algorithmic trading model CNN-TA using a 2-D ...

MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, have developed a quantum algorithm technology for deep convolutional neural network (CNN) exchange ...