Results 61 to 70 of about 2,475,679 (305)

Graph-regularized Bayesian broad learning system

智能科学与技术学报, 2022
As a feed forward neural network, broad learning system (BLS) has attracted much attention because of its high accuracy, fast training speed, and the ability to effectively replace deep learning methods.However, it is sensitive to the number of feature ...
Junwei DUAN, Lincan XU, Yujuan QUAN, Long CHEN, C.L.Philip CHEN   +4 more
doaj  

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

VertiBayes: learning Bayesian network parameters from vertically partitioned data with missing values

Complex & Intelligent Systems
Federated learning makes it possible to train a machine learning model on decentralized data. Bayesian networks are widely used probabilistic graphical models.
Florian van Daalen, Lianne Ippel, Andre Dekker, Inigo Bermejo   +3 more
doaj   +1 more source

Dynamic Bayesian Network Modeling Based on Structure Prediction for Gene Regulatory Network

IEEE Access, 2021
Gene regulatory network can intuitively reflect the interaction between genes, and an in-depth study of these relationships plays a significant role in the treatment and prevention of clinical diseases.
Luxuan Qu, Zhiqiong Wang, Chan Li, Shanghui Guo, Junchang Xin, Yuezhou Zhou, Weiyiqi Wang   +6 more
doaj   +1 more source

BRUNO: A Deep Recurrent Model for Exchangeable Data [PDF]

, 2018
We present a novel model architecture which leverages deep learning tools to perform exact Bayesian inference on sets of high dimensional, complex observations.
Dambre, Joni, Degrave, Jonas, Gal, Yarin, Gretton, Arthur, Huszár, Ferenc, Korshunova, Iryna   +5 more
core   +2 more sources

High-Order Stochastic Gradient Thermostats for Bayesian Learning of Deep Models

, 2015
Learning in deep models using Bayesian methods has generated significant attention recently. This is largely because of the feasibility of modern Bayesian methods to yield scalable learning and inference, while maintaining a measure of uncertainty in the
Carin, Lawrence, Chen, Changyou, Fan, Kai, Li, Chunyuan   +3 more
core   +1 more source

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

Bayesian optimized CNN ensemble for efficient potato blight detection using fuzzy image enhancement

Scientific Reports
Potato blight is a serious disease that affects potato crops and leads to substantial agricultural and economic losses. To enhance detection accuracy, we propose Bayesian Optimized CNN Weighted Ensemble Potato Blight Detection, a deep learning-based ...
Achin Jain, Arun Kumar Dubey, Vincent Shin-Hung Pan, Saoucene Mahfoudh, Turki A. Althaqafi, Varsha Arya, Wadee Alhalabi, Sunil K. Singh, Vanita Jain, Arvind Panwar, Sudhakar Kumar, Ching-Hsien Hsu, Brij B. Gupta   +12 more
doaj   +1 more source

Bayesian Nonparametric Dictionary Learning for Compressed Sensing MRI

, 2014
We develop a Bayesian nonparametric model for reconstructing magnetic resonance images (MRI) from highly undersampled k-space data. We perform dictionary learning as part of the image reconstruction process.
Ding, Xinghao, Fu, Xueyang, Huang, Yue, Lin, Qin, Paisley, John, Zhang, Xiao-ping   +5 more
core   +1 more source

????????? ?????? ???????????? ?????? ???????????? ?????? ??????????????? ???????????? ????????? ???????????? ?????? [PDF]

, 2020
Department of Computer Science and EngineeringAs deep learning has grown fast, so did the desire to interpret deep learning black boxes. As a result, many analysis tools have emerged to interpret it.
Lee, Ginkyeng
core