Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain Monte-Carlo ...

Campus News SpikeGPT: researcher releases code for largest-ever spiking neural network for language generation Assistant Professor of Electrical and Computer Engineering Jason Eshraghian and two ...

Artificial neural networks are a form of deep learning and one of the pillars of modern-day AI. The best way to really get a grip on how these things work is to build one.

Following is what you need for this book: This book is for machine learning practitioners and data scientists interested in learning about graph neural networks and their applications, as well as ...

KEMPNN (Knowledge-Embedded Message-Passing Neural Networks) is a message-passing neural networks that can be supervied togather with human-annotated knowledge annotations on the molecular graphs to ...

MISIM then uses a neural network to find other code that has a similar meaning. In a preprint, Gottschlich and his colleagues report that MISIM is 40 times more accurate than previous systems that ...