quantumzeitgeist

Bayesian Estimation Achieves Precision Scaling for Non-Markovian Parameters

For non-Markovian quantum systems, we demonstrate how Bayesian estimation—combined with reaction coordinate mapping—achieves ...
BioTechniques

AI meets immunology: uncovering mysteries of the immune system with machine learning

Could AI hold the key to answering questions that have stumped doctors and scientists for decades? A recent study at Cold ...
Aalto University

Doctoral Researcher in Probabilistic Machine Learning

We are looking for a doctoral researcher (PhD student) with a strong background in probabilistic machine learning, statistics, applied mathematics, computer science, or a related field, and strong ...
Fabbaloo

Could PAC Bayes Make Learning Based 3D Printer Control More Trustworthy?

A new control theory paper offers a way to certify learning based controllers from limited data, something that could ...
ascopubs.org

Bayesian Counterfactual Machine Learning Individualizes Radiation Modality Selection to Mitigate Immunosuppression

Tumor Site–Specific Radiation-Induced Lymphocyte Depletion Models After Fractionated Radiotherapy: Considerations of Model Structure From an Aggregate Data Meta-Analysis Lymphocytes play critical ...
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 ...
Frontiers

Enhancing disaster prediction with Bayesian deep learning: a robust approach for uncertainty estimation

Accurate disaster prediction combined with reliable uncertainty quantification is crucial for timely and effective decision-making in emergency management. However, traditional deep learning methods ...
C&EN

Bayesian Optimization with Gaussian Processes Assisted by Deep Learning for Material Designs

Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. Developing novel materials drives significant breakthroughs ...
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 ...