Results 51 to 60 of about 227,027 (269)

PREDICTION OF ALZHEIMER'S DISEASE USING BAYESIAN NEURAL NETWORKS

Компютерні системи та інформаційні технології
This article presents a methodology for optimizing Bayesian neural networks and their application to complex prediction tasks, with a focus on diagnosing Alzheimer’s disease.
Сергій ГЛАДІГОЛОВ, Олексій КОЗАЧКО   +1 more
doaj   +1 more source

Small-variance asymptotics for Bayesian neural networks [PDF]

, 2018
Bayesian neural networks (BNNs) are a rich and flexible class of models that have several advantages over standard feedforward networks, but are typically expensive to train on large-scale data.
Sankarapandian, Sivaramakrishnan
core  

Neuromorphic Electronics for Intelligence Everywhere: Emerging Devices, Flexible Platforms, and Scalable System Architectures

Advanced Materials, EarlyView.
The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj, Roshni Satheesh Babu, Yuxin Xia, Eva Bestelink, Radu Sporea, Nikos Hastas, Ioannis Zeimpekis, Dimitra G. Georgiadou, Sayani Majumdar   +8 more
wiley   +1 more source

Magnetic Textiles: A Review of Materials, Fabrication, Properties, and Applications

Advanced Materials Technologies, EarlyView.
Magnetic textiles (M‐textiles) are emerging as a programmable materials platform that merges magnetic matter with hierarchical textile structures. This article consolidates magnetic material classes, textile architectures, and fabrication and magnetization strategies, revealing structure–property–function relationships that govern magneto‐mechanical ...
Li Ke, Moses Kumi, Xiaoming Tao, Junhong Pu   +3 more
wiley   +1 more source

Triple equivalence for the emergence of biological intelligence

Communications Physics
Intelligent algorithms developed evolutionarily within neural systems are considered in this work. Mathematical analyses unveil a triple equivalence between canonical neural networks, variational Bayesian inference under a class of partially observable ...
Takuya Isomura
doaj   +1 more source

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

Bayesian Recurrent Neural Networks

, 2017
In this work we explore a straightforward variational Bayes scheme for Recurrent Neural Networks. Firstly, we show that a simple adaptation of truncated backpropagation through time can yield good quality uncertainty estimates and superior regularisation at only a small extra computational cost during training, also reducing the amount of parameters by
Fortunato, Meire, Blundell, Charles, Vinyals, Oriol   +2 more
openaire   +2 more sources

Bidirectional Process Prediction in the Laser‐Induced‐Graphene Production Using Blackbox Deep Learning

Advanced Materials Technologies, EarlyView.
This study shows that a lightweight blackbox neural network provides a practical, cost‐effective solution for bidirectional process prediction in laser‐induced graphene (LIG) fabrication. Achieving high predictive performance with minimal overhead, the approach democratizes machine learning (ML) for resource‐limited environments.
Maxim Polomoshnov, Nitheesh M. Nair, Gerardo Hernández‐Sosa, Markus Reischl   +3 more
wiley   +1 more source

Mean Field Bayes Backpropagation: scalable training of multilayer neural networks with binary weights [PDF]

, 2013
Significant success has been reported recently using deep neural networks for classification. Such large networks can be computationally intensive, even after training is over.
Meir, Ron, Soudry, Daniel
core  

Multimodal Actuation and Environment Adaptive Strategies of Bio‐Inspired Micro/Nanorobots in Precision Medicine

Advanced Robotics Research, EarlyView.
An introduction for multidrive and environment‐adaptive micro/nanorobotics: design and fabrication strategies, intelligent actuation, and their applications. Various intelligent actuation approaches—magnetic, acoustic, optical, chemical, and biological—can be synergistically designed to enhance flexibility and adaptive behavior for precision medicine ...
Aiqing Ma, Han Gong, Jiacheng Ouyang, Liqi Chen, Tianfang Kang, Yiran He, Guojun Huang, Sidi Zheng, Ting Yin, Hong Pan, Lintao Cai   +10 more
wiley   +1 more source