Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and ...

Inference latency was measured at 5–10ms per schematic on standard GPU hardware, confirming its applicability to interactive industrial Electronic Design Automation (EDA) workflows. These results ...

AI models now help fisheries securely analyze vast underwater images in-house. Trained on thousands of seafloor pictures, ...

For decades, scientists have looked to light as a way to speed up computing. Photonic neural networks—systems that use light instead of electricity to process information—promise faster speeds ...

Additional layers make the model better at extracting information and parsing out visual features, but that also makes it more difficult to know what the model is doing and what features it is using.

The third article in the Bitcoin Layer 2 series. This article covers the Lightning Network created by Joseph Poon and Tadge Dryja.

This paper introduces a novel graph neural layer, the dynamic connection layer (DCL), designed to address chemical graphs’ inaccurate atomic connection. The DCL layer employs a correction function, ...

For harder tasks, we’ll need to use a collection of many interconnected neurons — a neural network. Like an individual neuron, a network is just a mathematical function. Numbers go in, and other ...

Neural collapse (NC) reveals that the last layer of the network can capture data representations, leading to similar outputs for examples within the same class, while outputs for examples from ...

ABSTRACT: Convolutional auto-encoders have shown their remarkable performance in stacking deep convolutional neural networks for classifying image data during the past several years. However, they are ...