Ambient Diagnostics google
People can usually sense troubles in a car from noises, vibrations, or smells. An experienced driver can even tell where the problem is. We call this kind of skill ‘Ambient Diagnostics’. Ambient Diagnostics is an emerging field that is aimed at detecting abnormities from seemly disconnected ambient data that we take for granted. For example, the human body is a rich ambient data source: temperature, pulses, gestures, sound, forces, moisture, et al. Also, many electronic devices provide pervasive ambient data streams, such as mobile phones, surveillance cameras, satellite images, personal data assistants, wireless networks and so on. …

Local Error Driven and Associative Biologically Realistic Algorithm (leabra) google
The algorithm Leabra (local error driven and associative biologically realistic algorithm) allows for the construction of artificial neural networks that are biologically realistic and balance supervised and unsupervised learning within a single framework. …

Coordinate Descent Algorithms (CDA) google
This monograph presents a class of algorithms called coordinate descent algorithms for mathematicians, statisticians, and engineers outside the field of optimization. This particular class of algorithms has recently gained popularity due to their effectiveness in solving large-scale optimization problems in machine learning, compressed sensing, image processing, and computational statistics. Coordinate descent algorithms solve optimization problems by successively minimizing along each coordinate or coordinate hyperplane, which is ideal for parallelized and distributed computing. Avoiding detailed technicalities and proofs, this monograph gives relevant theory and examples for practitioners to effectively apply coordinate descent to modern problems in data science and engineering. To keep the primer up-to-date, we intend to publish this monograph only after no additional topics need to be added and we foresee no further major advances in the area. …

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