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Sparse models: theory and applications
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Retų struktūrų modeliai: teorija ir taikymai
Title: Sparse models: theory and applications
Description:
Retų struktūrų modeliai: teorija ir taikymai.
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Selection of Injectable Drug Product Composition using Machine Learning Models (Preprint)
Selection of Injectable Drug Product Composition using Machine Learning Models (Preprint)
BACKGROUND
As of July 2020, a Web of Science search of “machine learning (ML)” nested within the search of “pharmacokinetics or pharmacodynamics” yielded over 100...
Sparse Unmixing of Hyperspectral Data with Noise Level Estimation
Sparse Unmixing of Hyperspectral Data with Noise Level Estimation
Recently, sparse unmixing has received particular attention in the analysis of hyperspectral images (HSIs). However, traditional sparse unmixing ignores the different noise levels ...
Grounded Theory
Grounded Theory
Widely used in social work, grounded theory is one of the oldest and best-known qualitative research methods. Even so, it is often misunderstood. Created at a time when positivism ...
Sparse connectivity enables efficient information processing in cortex-like artificial neural networks
Sparse connectivity enables efficient information processing in cortex-like artificial neural networks
Neurons in cortical networks are very sparsely connected; even neurons whose axons and dendrites overlap are highly unlikely to form a synaptic connection. What is the relevance of...
Game Theory in Business Ethics: Bad Ideology or Bad Press?
Game Theory in Business Ethics: Bad Ideology or Bad Press?
Solomon’s article and Binmore’s response exemplify a standard exchange between the game theorist and those critical of applying game theory to ethics. The critic of game theory lis...
Joint sparse optimization: lower-order regularization method and application in cell fate conversion
Joint sparse optimization: lower-order regularization method and application in cell fate conversion
Abstract
Multiple measurement signals are commonly collected in practical applications, and joint sparse optimization adopts the synchronous effect within multiple m...
Optimized Sparse Polynomial Chaos Expansion With Entropy Regularization
Optimized Sparse Polynomial Chaos Expansion With Entropy Regularization
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Sparse Polynomial Chaos Expansion(PCE) is widely used in various engineering fields to quantitatively analyse the influence of uncertainty, while alleviate the pro...
Extreme Learning Machines as Encoders for Sparse Reconstruction
Extreme Learning Machines as Encoders for Sparse Reconstruction
Reconstruction of fine-scale information from sparse data is often needed in practical fluid dynamics where the sensors are typically sparse and yet, one may need to learn the unde...