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Markov Chains

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Layer adaptive node selection in Bayesian neural networks: Statistical guarantees and implementation details.

Neural networks : the official journal of the International Neural Network Society
Sparse deep neural networks have proven to be efficient for predictive model building in large-scale studies. Although several works have studied theoretical and numerical properties of sparse neural architectures, they have primarily focused on the ...

Adaptive neural network control for Markov jumping systems against deception attacks.

Neural networks : the official journal of the International Neural Network Society
This paper proposes an innovative approach for mitigating the effects of deception attacks in Markov jumping systems by developing an adaptive neural network control strategy. To address the challenge of dual-mode monitoring mechanisms, two independe...

Markov chain stochastic DCA and applications in deep learning with PDEs regularization.

Neural networks : the official journal of the International Neural Network Society
This paper addresses a large class of nonsmooth nonconvex stochastic DC (difference-of-convex functions) programs where endogenous uncertainty is involved and i.i.d. (independent and identically distributed) samples are not available. Instead, we ass...

Automated model building and protein identification in cryo-EM maps.

Nature
Interpreting electron cryo-microscopy (cryo-EM) maps with atomic models requires high levels of expertise and labour-intensive manual intervention in three-dimensional computer graphics programs. Here we present ModelAngelo, a machine-learning approa...

Research on Pig Sound Recognition Based on Deep Neural Network and Hidden Markov Models.

Sensors (Basel, Switzerland)
In order to solve the problem of low recognition accuracy of traditional pig sound recognition methods, deep neural network (DNN) and Hidden Markov Model (HMM) theory were used as the basis of pig sound signal recognition in this study. In this study...

Machine Learning Deciphered Molecular Mechanistics with Accurate Kinetic and Thermodynamic Prediction.

Journal of chemical theory and computation
Time-lagged independent component analysis (tICA) and the Markov state model (MSM) have been extensively employed for extracting conformational dynamics and kinetic community networks from unbiased trajectory ensembles. However, these techniques may ...

Aorta Segmentation in 3D CT Images by Combining Image Processing and Machine Learning Techniques.

Cardiovascular engineering and technology
PURPOSE: Aorta segmentation is extremely useful in clinical practice, allowing the diagnosis of numerous pathologies, such as dissections, aneurysms and occlusive disease. In such cases, image segmentation is prerequisite for applying diagnostic algo...

Cost-Effectiveness of Artificial Intelligence-Based Opportunistic Compression Fracture Screening of Existing Radiographs.

Journal of the American College of Radiology : JACR
PURPOSE: Osteoporotic vertebral compression fractures (OVCFs) are a highly prevalent source of morbidity and mortality, and preventive treatment has been demonstrated to be both effective and cost effective. To take advantage of the information avail...

An intelligent model to decode students' behavioral states in physical education using back propagation neural network and Hidden Markov Model.

BMC psychology
This paper highlights the need for intelligent analysis of students' behavioral states in physical education tasks. The hand-ring inertial data is used to identify students' motion sequence states. First, statistical feature extraction is performed b...

SemiHAR: Improving Semisupervised Human Activity Recognition via Multitask Learning.

IEEE transactions on neural networks and learning systems
Semisupervised human activity recognition (SemiHAR) has attracted attention in recent years from various domains, such as digital health and ambient intelligence. Currently, it still faces two challenges. For one thing, discriminative features may ex...