Tag: Bayesian Deep Learning

AI Machine Learning & Data Science Research

NNAISENSE’s New Class of Generative Model: Bayesian Flow Networks Break Barriers in Handing Discrete Data

A NNAISENSE research team introduces a novel class of generative models known as Bayesian Flow Networks (BFNs). These BFNs combine the power of Bayesian inference with neural networks in an iterative modeling process, enabling successful application to continuous, discretized, and discrete data while maintaining competitive performance.

AI Machine Learning & Data Science Research

Standford U’s MAPTree: Redefining Decision Trees – Precision, Speed, and Efficiency Unleashed

In a new paper MAPTree: Beating “Optimal” Decision Trees with Bayesian Decision Trees, a Stanford University research team introduces MAPTree, an algorithm that confidently uncovers the maximum a posteriori tree within Bayesian Classification and Regression Trees (BCART) posterior, achieving strong performance with significantly leaner and faster trees.

AI Machine Learning & Data Science Research

New Study Revisits Laplace Approximation, Validating It as an ‘Effortless’ Method for Bayesian Deep Learning

In the new paper Laplace Redux — Effortless Bayesian Deep Learning, a research team from the University of Cambridge, University of Tübingen, ETH Zurich and DeepMind conducts extensive experiments demonstrating that the Laplace approximation (LA) is a simple and cost-efficient yet competitive approximation method for inference in Bayesian deep learning.