AIMC Topic: Machine Learning

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Advanced mean-field theory of the restricted Boltzmann machine.

Physical review. E, Statistical, nonlinear, and soft matter physics
Learning in restricted Boltzmann machine is typically hard due to the computation of gradients of log-likelihood function. To describe the network state statistics of the restricted Boltzmann machine, we develop an advanced mean-field theory based on...

Real-time, adaptive machine learning for non-stationary, near chaotic gasoline engine combustion time series.

Neural networks : the official journal of the International Neural Network Society
Fuel efficient Homogeneous Charge Compression Ignition (HCCI) engine combustion timing predictions must contend with non-linear chemistry, non-linear physics, period doubling bifurcation(s), turbulent mixing, model parameters that can drift day-to-da...

A hybrid method for airway segmentation and automated measurement of bronchial wall thickness on CT.

Medical image analysis
Inflammatory and infectious lung diseases commonly involve bronchial airway structures and morphology, and these abnormalities are often analyzed non-invasively through high resolution computed tomography (CT) scans. Assessing airway wall surfaces an...

Learning Slowness in a Sparse Model of Invariant Feature Detection.

Neural computation
Primary visual cortical complex cells are thought to serve as invariant feature detectors and to provide input to higher cortical areas. We propose a single model for learning the connectivity required by complex cells that integrates two factors tha...

Experimental design strategy: weak reinforcement leads to increased hit rates and enhanced chemical diversity.

Journal of chemical information and modeling
High Throughput Screening (HTS) is a common approach in life sciences to discover chemical matter that modulates a biological target or phenotype. However, low assay throughput, reagents cost, or a flowchart that can deal with only a limited number o...

Estimating Energy Expenditure With Multiple Models Using Different Wearable Sensors.

IEEE journal of biomedical and health informatics
This paper presents an approach to designing a method for the estimation of human energy expenditure (EE). The approach first evaluates different sensors and their combinations. After that, multiple regression models are trained utilizing data from d...

Methods for discovery and characterization of cell subsets in high dimensional mass cytometry data.

Methods (San Diego, Calif.)
The flood of high-dimensional data resulting from mass cytometry experiments that measure more than 40 features of individual cells has stimulated creation of new single cell computational biology tools. These tools draw on advances in the field of m...

Evaluation and integration of cancer gene classifiers: identification and ranking of plausible drivers.

Scientific reports
The number of mutated genes in cancer cells is far larger than the number of mutations that drive cancer. The difficulty this creates for identifying relevant alterations has stimulated the development of various computational approaches to distingui...

Asymptotic accuracy of Bayesian estimation for a single latent variable.

Neural networks : the official journal of the International Neural Network Society
In data science and machine learning, hierarchical parametric models, such as mixture models, are often used. They contain two kinds of variables: observable variables, which represent the parts of the data that can be directly measured, and latent v...