Scientists debut a generative AI model that could create molecules addressing hard-to-treat diseases

More than 300 people across academia and industry spilled into an auditorium to attend a BoltzGen seminar on Thursday, Oct.
Tech Xplore

New model measures how AI sycophancy affects chatbot accuracy and rationality

If you've spent any time with ChatGPT or another AI chatbot, you've probably noticed they are intensely, almost overbearingly ...
IEEE

Fast Robust Sparse Bayesian Learning Image Reconstruction Model Based on Generalized Approximate Message Passing

Abstract: Sparse Bayesian learning (SBL) is an algorithm for high-dimensional data processing based on Bayesian statistical theory. Its goal is to improve the generalization ability and efficiency of ...
Open Knowledge Repository - World Bank

Maldives: Decision Support for Coral Reef and Climate Resilience Using Bayesian Networks

The coral reef ecosystems of the Maldives are critical to the nation’s ecological integrity, economic development, and ...

Yixian Jiang Integrates Bayesian Networks and Metadata Standards to Advance Intelligent Digital Object Systems

A deep learning framework enhances medical image recognition by optimizing RNN architectures with LSTM, GRU, multimodal fusion, and CNN ...
EurekAlert!

An interpretable machine learning model for predicting 5‐year survival in breast cancer based on integration of proteomics and clinical data

Breast cancer is a highly heterogeneous malignancy among women worldwide. Traditional prognostic models relying solely on ...

Mechanistic model can predict biological community development across ecosystems

Biological communities are rarely stable. Their composition is constantly changing, depending on the environmental conditions ...
Frontiers

Toward interpretable expected goals modeling using Bayesian mixed models

Empowered by technological progress, sports teams and bookmakers strive to understand relationships between player and team activity and match outcomes. For this purpose, the probability of an event ...
JSTOR Daily

Learning from a lot: Empirical Bayes for high-dimensional model-based prediction

Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, ...
C&EN

ProcessOptimizer, an Open-Source Python Package for Easy Optimization of Real-World Processes Using Bayesian Optimization: Showcase of Features and Example of Use

ProcessOptimizer is a Python package designed to provide easy access to advanced machine learning techniques, specifically Bayesian optimization using, e.g., Gaussian processes. Aimed at ...
Scientific Research Publishing

Vehtari, A., Gelman, A. and Gabry, J. (2016) Practical Bayesian Model Evaluation Using Leave-One-Out Cross-Validation and WAIC. Statistics and Computing, 27, 1413-1432.

ABSTRACT: This study explores the application of Bayesian econometrics in policy evaluation through theoretical analysis. The research first reviews the theoretical foundations of Bayesian methods, ...