Results 11 to 20 of about 1,077,907 (260)
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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Sparse representations of high dimensional neural data
Scientific Reports, 2022 Conventional Vector Autoregressive (VAR) modelling methods applied to high dimensional neural time series data result in noisy solutions that are dense or have a large number of spurious coefficients.
Sandeep K. Mody, Govindan Rangarajan
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Searching for best lower dimensional visualization angles for high dimensional RNA-Seq data [PDF]
PeerJ, 2018 The accumulation of RNA sequencing (RNA-Seq) gene expression data in recent years has resulted in large and complex data sets of high dimensions. Exploratory analysis, including data mining and visualization, reveals hidden patterns and potential ...
Wanli Zhang, Yanming Di
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High Density Subspace Clustering Algorithm for High Dimensional Data
Journal of Harbin University of Science and Technology, 2020 Highdimensional data have the characteristics of sparsity and vulnerability to dimension disaster, which makes it is difficult to ensure the precision and efficiency of high dimensional data clustering Therefore the method of subspace clustering is ...
WAN Jing +3 more
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Missing Data Imputation with High-Dimensional Data
The American Statistician, 2023 Imputation of missing data in high-dimensional datasets with more variables P than samples N, P≫N, is hampered by the data dimensionality. For multivariate imputation, the covariance matrix is ill conditioned and cannot be properly estimated. For fully conditional imputation, the regression models for imputation cannot include all the variables.
Alberto Brini, Edwin R. van den Heuvel
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Attention-driven tree-structured convolutional LSTM for high dimensional data understanding
Frontiers in Physics, 2023 Modeling sequential information for image sequences is a vital step of various vision tasks and convolutional long short-term memory (ConvLSTM) has demonstrated its superb performance in such spatiotemporal problems.
Yi Lu +8 more
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Knowledge Transfer Between Artificial Intelligence Systems
Frontiers in Neurorobotics, 2018 We consider the fundamental question: how a legacy “student” Artificial Intelligent (AI) system could learn from a legacy “teacher” AI system or a human expert without re-training and, most importantly, without requiring significant computational ...
Ivan Y. Tyukin +6 more
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