Results 1 to 10 of about 1,150,095 (115)
A hybrid anomaly detection method for high dimensional data [PDF]
PeerJ Computer Science, 2023 Anomaly detection of high-dimensional data is a challenge because the sparsity of the data distribution caused by high dimensionality hardly provides rich information distinguishing anomalous instances from normal instances. To address this, this article
Xin Zhang, Pingping Wei, Qingling Wang
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Telescope indexing for k-nearest neighbor search algorithms over high dimensional data & large data sets [PDF]
Scientific ReportsWhen k-Nearest-Neighbors ( $$k$$ -NN) was conceived more than 70 years ago, computation, as we use it now, would be hardly recognizable. Since then, technology has improved by orders of magnitude, including unprecedented connectivity.
Madhavan K R +3 more
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Artificial Neural Network Model with Astrocyte-Driven Short-Term Memory
Biomimetics, 2023 In this study, we introduce an innovative hybrid artificial neural network model incorporating astrocyte-driven short-term memory. The model combines a convolutional neural network with dynamic models of short-term synaptic plasticity and astrocytic ...
Ilya A. Zimin +2 more
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Information Encoding in Bursting Spiking Neural Network Modulated by Astrocytes
Entropy, 2023 We investigated a mathematical model composed of a spiking neural network (SNN) interacting with astrocytes. We analysed how information content in the form of two-dimensional images can be represented by an SNN in the form of a spatiotemporal spiking ...
Sergey V. Stasenko, Victor B. Kazantsev
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Deep forecasting of translational impact in medical research
Patterns, 2022 Summary: The value of biomedical research—a $1.7 trillion annual investment—is ultimately determined by its downstream, real-world impact, whose predictability from simple citation metrics remains unquantified. Here we sought to determine the comparative
Amy P.K. Nelson +12 more
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Features Clustering Around Latent Variables for High Dimensional Data [PDF]
E3S Web of Conferences, 2021 Clustering of variables is the task of grouping similar variables into different groups. It may be useful in several situations such as dimensionality reduction, feature selection, and detect redundancies.
Ghizlane Ez-Zarrad +2 more
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Industry-scale application and evaluation of deep learning for drug target prediction
Journal of Cheminformatics, 2020 Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling.
Noé Sturm +18 more
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Discriminant methods for high dimensional data [PDF]
Songklanakarin Journal of Science and Technology (SJST), 2019 The main purpose of discriminant analysis is to enable classification of new observations into one of g classes or populations. Discriminant methods suffer when applied to high dimensional data because the sample covariance matrix is singular.
Poompong Kaewumpai, Samruam Chongcharoen
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High-Dimensional Brain in a High-Dimensional World: Blessing of Dimensionality
Entropy, 2020 High-dimensional data and high-dimensional representations of reality are inherent features of modern Artificial Intelligence systems and applications of machine learning.
Alexander N. Gorban +2 more
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High-Dimensional Separability for One- and Few-Shot Learning
Entropy, 2021 This work is driven by a practical question: corrections of Artificial Intelligence (AI) errors. These corrections should be quick and non-iterative. To solve this problem without modification of a legacy AI system, we propose special ‘external’ devices,
Alexander N. Gorban +4 more
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