Nanowerk

Atomic jitters power a new kind of probabilistic AI chip

Thermal noise in magnetic tunnel junctions, usually suppressed, now serves as a tunable source of randomness for Bayesian ...
diginomica

Enterprise hits and misses - Bayesian uncertainty makes for better AI, tokenomics gets demystified, and Meta's AI Avocado isn't ripe yet

This week - Oracle earnings punch back on the SaaS-is-dead narrative, but Adobe's CEO departure shows that market upheaval ...
Scientific Research Publishing

Comparative Analysis of Neural Networks and Naive Bayes for Multilingual Text Identification ()

1 Department of Computer Science, Rochester Institute of Technology, Rochester, USA. 2 Department of Computer Science, Rutgers University, New Brunswick, USA. Language identification is a fundamental ...
TCTMD

Case Examples of Bayesian Study Designs Used to Accelerate Assessments of Device Safety and Efficacy in SHD

Receive the the latest news, research, and presentations from major meetings right to your inbox. TCTMD ® is produced by the Cardiovascular Research Foundation ® (CRF). CRF ® is committed to igniting ...
IEEE

Improving Transferability of Adversarial Examples via Bayesian Attacks

Abstract: The transferability of adversarial examples allows for the attack on unknown deep neural networks (DNNs), posing a serious threat to many applications and attracting great attention. In this ...
Frontiers

A guide to bayesian networks software for structure and parameter learning, with a focus on causal discovery tools

A representation of the cause-effect mechanism is needed to enable artificial intelligence to represent how the world works. Bayesian Networks (BNs) have proven to be an effective and versatile tool ...
GitHub

Protein Sequence Modelling with Bayesian Flow Networks

Welcome to the inference code for the paper "Protein Sequence Modelling with Bayesian Flow Networks". With this code, you can sample from our trained models ProtBFN, for general proteins, and AbBFN, ...