Results 71 to 80 of about 2,475,679 (305)
Advanced Functional Materials, EarlyView.This study establishes a materials‐driven framework for entropy generation within standard CMOS technology. By electrically rebalancing gate‐oxide traps and Si‐channel defects in foundry‐fabricated FDSOI transistors, the work realizes in‐materia control of temporal correlation – achieving task adaptive entropy optimization for reinforcement learning ...
Been Kwak +14 more
wiley +1 more source
Local Bayesian Dirichlet mixing of imperfect models
Scientific Reports, 2023 To improve the predictability of complex computational models in the experimentally-unknown domains, we propose a Bayesian statistical machine learning framework utilizing the Dirichlet distribution that combines results of several imperfect models. This
Vojtech Kejzlar +2 more
doaj +1 more source
Advanced Functional Materials, EarlyView.Permanent magnets derive their extraordinary strength from deep, universal electronic‐structure principles that control magnetization, anisotropy, and intrinsic performance. This work uncovers those governing rules, examines modern modeling and AI‐driven discovery methods, identifies critical bottlenecks, and reveals electronic fingerprints shared ...
Prashant Singh
wiley +1 more source
Bayesian machine learning for financial modeling [PDF]
, 2021 Machine Learning (ML) is so pervasive in our todays life that we don't even realise that, more often than expected, we are using systems based on it. It is also evolving faster than ever before. When deploying ML systems that make decisions on their own, we need to think about their ignorance of our uncertain world.
openaire +3 more sources
Accelerated Discovery of High Performance Ni3S4/Ni3Mo HER Catalysts via Bayesian Optimization
Advanced Functional Materials, EarlyView.Integrated workflow accelerates the catalyst discovery of hydrogen evolution reaction via Bayesian optimization. An experiment‐trained surrogate model proposes synthesis conditions, guiding iterative refinement using electrochemical performance metrics.
Namuersaihan Namuersaihan +9 more
wiley +1 more source
Artificial Intelligence as the Next Visionary in Liquid Crystal Research
Advanced Functional Materials, EarlyView.The functions of AI in the research laboratory are becoming increasingly sophisticated, allowing the entire process of hypothesis formulation, material design, synthesis, experimental design, and reiterative testing to be automated. In our work, we conceive how the incorporation of AI in the laboratory environment will transform the role and ...
Mert O. Astam +2 more
wiley +1 more source
Bayesian Semi-supervised Learning with Graph Gaussian Processes [PDF]
, 2018 We propose a data-efficient Gaussian process-based Bayesian approach to the semi-supervised learning problem on graphs. The proposed model shows extremely competitive performance when compared to the state-of-the-art graph neural networks on semi ...
Colombo, Nicolo +2 more
core +1 more source
Bayesian Sparsification for Deep Neural Networks With Bayesian Model Reduction
IEEE AccessDeep learning’s immense capabilities are often constrained by the complexity of its models, leading to an increasing demand for effective sparsification techniques.
Dimitrije Markovic +2 more
doaj +1 more source
Advanced Healthcare Materials, EarlyView.We developed a micro‐sized, biocompatible implant for postoperative sustained delivery of anti‐fibrotic antibodies in glaucoma surgery. Machine learning‐guided optimization of polymer composition, implant geometry, and porosity enabled precise control of drug release.
Mengqi Qin +5 more
wiley +1 more source
Psychological evidence suggests that people’s learning behavior is often prone to a “myside bias”or “irrational belief persistence”in contrast to learning behavior exclusively based on objective data.
Ludwig, Alexander, Zimper, Alexander
core +3 more sources