This collection welcomes submissions on explainability techniques for deep learning neural networks, encompassing diverse neural architectures and ensuring broad applicability to different domains.

Neural networks are now applied across the spectrum of AI applications while deep learning is reserved for more specialized or advanced AI use cases.

For more than eighty years, deep learning has relied on a simplified model of brain function. Now, a Pittsburgh startup ...

We will create a Deep Neural Network python from scratch. We are not going to use Tensorflow or any built-in model to write the code, but it's entirely from scratch in python. We will code Deep Neural ...

The recently published book Understanding Deep Learning by [Simon J. D. Prince] is notable not only for focusing primarily on the concepts behind Deep Learning — which should make it highly a… ...

Liquid neural networks can spur new innovations in AI and are particularly exciting in areas where traditional deep learning models struggle.

By tapping into a decades-old mathematical principle, researchers are hoping that Kolmogorov-Arnold networks will facilitate scientific discovery.

If deep neural networks exhibit absorbing phase transitions, then universal scaling laws may apply, providing a unified framework for describing how they function.

This study presents a valuable application of a video-text alignment deep neural network model to improve neural encoding of naturalistic stimuli in fMRI. The authors found that models based on ...

As the world grapples with climate change and dwindling fossil fuel reserves, biodiesel emerges as a promising renewable alternative to conventional ...