OpenAI on Saturday announced the OpenAI Partner Network — its first formal global partner program — investing $150 million to help consulting firms, systems integrators, and technology specialists ...

CardioMorphNet is a recurrent Bayesian deep learning framework for 3D cardiac shape-guided deformable registration using short-axis (SAX) cine CMR images. It employs a recurrent variational ...

ABSTRACT: This work introduces a novel Bayesian inspired regression method for the simultaneous estimation of model parameters and data uncertainties. The key mathematical result of this framework is ...

In this tutorial, we implement an advanced Bayesian hyperparameter optimization workflow using Hyperopt and the Tree-structured Parzen Estimator (TPE) algorithm. We construct a conditional search ...

Lotteries are hard to win. The odds of hitting the Powerball jackpot are so tiny that, as a CNN commenter once put it, you have a better chance of becoming an astronaut, dating a supermodel, and ...

In this tutorial, we build a complete scientific discovery agent step by step and experience how each component works together to form a coherent research workflow. We begin by loading our literature ...

ABSTRACT: This paper investigates the application of machine learning techniques to optimize complex spray-drying operations in manufacturing environments. Using a mixed-methods approach that combines ...

Synthetic dataset outputs for public analysis without privacy risk. Part of my current workflow as survey leader of the Data Engineering Pilipinas group. Comparable distributions per column: based on ...

Abstract: Operational technology (OT) systems face increasing cybersecurity risks from adversarial behavior. This paper describes the development of a Bayesian network risk model to enhance the ...

Bayesian Network (BN) [15] is a probabilistic graphical model that represents a set of random variables and their conditional probabilities via a directed acyclic graph (DAG). In these graphical ...