AIMC Topic: Bayes Theorem

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Gloss perception: Searching for a deep neural network that behaves like humans.

Journal of vision
The visual computations underlying human gloss perception remain poorly understood, and to date there is no image-computable model that reproduces human gloss judgments independent of shape and viewing conditions. Such a model could provide a powerfu...

Statistical Inference for Clustering Results Interpretation in Clinical Practice.

Studies in health technology and informatics
The relevance of this study lies in improvement of machine learning models understanding. We present a method for interpreting clustering results and apply it to the case of clinical pathways modeling. This method is based on statistical inference an...

RoBoT: a robust Bayesian hypothesis testing method for basket trials.

Biostatistics (Oxford, England)
A basket trial in oncology encompasses multiple "baskets" that simultaneously assess one treatment in multiple cancer types or subtypes. It is well-recognized that hierarchical modeling methods, which adaptively borrow strength across baskets, can im...

Regularized Bayesian transfer learning for population-level etiological distributions.

Biostatistics (Oxford, England)
Computer-coded verbal autopsy (CCVA) algorithms predict cause of death from high-dimensional family questionnaire data (verbal autopsy) of a deceased individual, which are then aggregated to generate national and regional estimates of cause-specific ...

Machine learning for phytopathology: from the molecular scale towards the network scale.

Briefings in bioinformatics
With the increasing volume of high-throughput sequencing data from a variety of omics techniques in the field of plant-pathogen interactions, sorting, retrieving, processing and visualizing biological information have become a great challenge. Within...

A comprehensive overview and critical evaluation of gene regulatory network inference technologies.

Briefings in bioinformatics
Gene regulatory network (GRN) is the important mechanism of maintaining life process, controlling biochemical reaction and regulating compound level, which plays an important role in various organisms and systems. Reconstructing GRN can help us to un...

A Bayesian optimization approach for rapidly mapping residual network function in stroke.

Brain : a journal of neurology
Post-stroke cognitive and linguistic impairments are debilitating conditions, with limited therapeutic options. Domain-general brain networks play an important role in stroke recovery and characterizing their residual function with functional MRI has...

Asynchronous parallel Bayesian optimization for AI-driven cloud laboratories.

Bioinformatics (Oxford, England)
MOTIVATION: The recent emergence of cloud laboratories-collections of automated wet-lab instruments that are accessed remotely, presents new opportunities to apply Artificial Intelligence and Machine Learning in scientific research. Among these is th...

Joint Associations of Multiple Dietary Components With Cardiovascular Disease Risk: A Machine-Learning Approach.

American journal of epidemiology
The human diet consists of a complex mixture of components. To realistically assess dietary impacts on health, new statistical tools that can better address nonlinear, collinear, and interactive relationships are necessary. Using data from 1,928 heal...

Probabilistic Contextual and Structural Dependencies Learning in Grammar-Based Genetic Programming.

Evolutionary computation
Genetic Programming is a method to automatically create computer programs based on the principles of evolution. The problem of deceptiveness caused by complex dependencies among components of programs is challenging. It is important because it can mi...