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

This repository provides unofficial binary wheels for Pygame for Python on Windows. The files are unofficial (meaning: informal, unrecognized, personal, unsupported, no warranty, no liability, ...

Learn how to build a fully connected, feedforward deep neural network from scratch in Python! This tutorial covers the theory, forward propagation, backpropagation, and coding step by step for a hands ...

Operator learning is a transformative approach in scientific computing. It focuses on developing models that map functions to other functions, an essential aspect of solving partial differential ...

On Monday, California Gov. Gavin Newsom signed into law SB 1223, amending the California Consumer Privacy Act (CCPA) to include neural data as personal sensitive ...

The simplified approach makes it easier to see how neural networks produce the outputs they do. A tweak to the way artificial neurons work in neural networks could make AIs easier to decipher.

This work will be of interest to the motor control community as well as neuroAI researchers interested in how bodies constrain neural circuit function. The authors present "MotorNet", a useful ...

Abstract: When programmers write project code, they may copy or reference some open-source code, which may include defective code, causing vulnerabilities in the project. This causes a potential ...