Results 51 to 60 of about 199,039 (272)

Bayesian Neural Networks via MCMC: A Python-Based Tutorial

IEEE Access
Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain Monte-Carlo (MCMC) sampling methods are used to implement Bayesian ...
Rohitash Chandra, Joshua Simmons
doaj   +1 more source

A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions

Advanced Engineering Materials, EarlyView.
This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice, Krzysztof S. Stopka, Michael D. Sangid   +2 more
wiley   +1 more source

Physics-constrained bayesian neural network for fluid flow reconstruction with sparse and noisy data

Theoretical and Applied Mechanics Letters, 2020
: In many applications, flow measurements are usually sparse and possibly noisy. The reconstruction of a high-resolution flow field from limited and imperfect flow information is significant yet challenging. In this work, we propose an innovative physics-
Luning Sun, Jian-Xun Wang
doaj   +1 more source

Deep Bayesian Active Semi-Supervised Learning [PDF]

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018
In many applications the process of generating label information is expensive and time consuming. We present a new method that combines active and semi-supervised deep learning to achieve high generalization performance from a deep convolutional neural network with as few known labels as possible.
Rottmann, Matthias, Kahl, Karsten, Gottschalk, Hanno   +2 more
openaire   +2 more sources

Integrative Approaches for DNA Sequence‐Controlled Functional Materials

Advanced Functional Materials, EarlyView.
DNA is emerging as a programmable building block for functional materials with applications in biomimicry, biochemical, and mechanical information processing. The integration of simulations, experiments, and machine learning is explored as a means to bridge DNA sequences with macroscopic material properties, highlighting current advances and providing ...
Aaron Gadzekpo, Ewa Anna Oprzeska‐Zingrebe, Mariana Kozlowska, Lennart Hilbert, Iliya D. Stoev   +4 more
wiley   +1 more source

In Materia Shaping of Randomness with a Standard Complementary Metal‐Oxide‐Semiconductor Transistor for Task‐Adaptive Entropy Generation

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, Ryun‐Han Koo, Youngchan Cho, Changhyeon Han, Dongbin Kim, Soi Jeong, Yunho Shin, Joonhyeok Choi, Jiseong Im, Jonghyun Ko, Jong‐Ho Lee, Jangsaeng Kim, Youngho Kang, Wonjun Shin, Daewoong Kwon   +14 more
wiley   +1 more source

Collapsed Inference for Bayesian Deep Learning

, 2023
Bayesian neural networks (BNNs) provide a formalism to quantify and calibrate uncertainty in deep learning. Current inference approaches for BNNs often resort to few-sample estimation for scalability, which can harm predictive performance, while its alternatives tend to be computationally prohibitively expensive. We tackle this challenge by revealing a
Zeng, Zhe, Broeck, Guy Van den
openaire   +2 more sources

Universal Electronic‐Structure Relationship Governing Intrinsic Magnetic Properties in Permanent Magnets

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

A COMPARATIVE ANALYSIS OF WEB INFORMATION EXTRACTION TECHNIQUES DEEP LEARNING vs. NAÏVE BAYES vs. BACK PROPAGATION NEURAL NETWORKS IN WEB DOCUMENT EXTRACTION [PDF]

ICTACT Journal on Soft Computing, 2016
Web mining related exploration is getting the chance to be more essential these days in view of the reason that a lot of information is overseen through the web. Web utilization is expanding in an uncontrolled way.
J. Sharmila, A. Subramani
doaj  

Uncertainty Quantification for MLP-Mixer Using Bayesian Deep Learning

Applied Sciences, 2023
Convolutional neural networks (CNNs) have become a popular choice for various image classification applications. However, the multi-layer perceptron mixer (MLP-Mixer) architecture has been proposed as a promising alternative, particularly for large ...
Abdullah A. Abdullah, Masoud M. Hassan, Yaseen T. Mustafa   +2 more
doaj   +1 more source