Constrained deep learning is an advanced approach to training deep neural networks by incorporating domain-specific constraints into the learning process.

To test, we will use real functions to generate sample input/output pairs (x, f(x)) and use these pairs to train a neural network written in MATLAB. Then we plan to test how well the trained network ...

A critical procedure in diagnosing atrial fibrillation is the creation of electro-anatomic activation maps. Current methods generate these mappings from interpolation using a few sparse data points ...

Western researchers have developed a novel technique using math to understand exactly how neural networks make decisions ... be useful far beyond this first example." The implications of the ...

In this paper, a novel fusion method on the multimodal medical images exploiting convolutional neural network (CNN) and extreme learning machine (ELM ... All the simulation experiments are performed ...

Learn More A new neural-network architecture developed by researchers ... attention and memory modules to complement each other. For example, the attention layers can use the historical and ...

Abstract: This paper discusses finite-time synchronization of complex-valued neural networks (CVNNs) with infinite delays ... Finally, to confirm the effectiveness of the theoretical outcomes, two ...

Traditional processors can struggle with these requirements, leading to high energy consumption, increased latency, and throughput bottlenecks. A cornerstone of neural network computation is the ...

It’s genuinely clever, and it uses AI in real time to work it all out – there are basically loads of neural networks processing the game data live as you play, learning what’s in the scene ...

Red Hat, the IBM-owned open-source software giant, has completed its acquisition of Neural Magic, a pioneering artificial intelligence (AI) optimization startup. Initially announced in November ...