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Jun 15, 2020 · In this paper, the problem of orthogonality is first investigated through conventional k-means of images, where images are to be processed as vectors.
Sep 6, 2024 · In conventional machine learning applications, each data attribute is assumed to be orthogonal to others. Namely, every pair of dimension is ...
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Many alternatives of convolutional-logic are also discussed for spatio-temporal information preservation, including a spatio-temporal hypercomplex encoding ...
Other alternatives of convolutional-logic are also discussed for spatio-temporal information preservation, including a spatio-temporal hypercomplex encoding ...
Sep 30, 2022 · The authors suggest the use of complex, hypercomplex, and GA-based approaches and encoding schemes, to better capture invariance under rotation ...
It proposes an efficient framework in modelling with machine learning algorithms spatio-temporal fields measured on irregular monitoring networks, accounting ...
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We develop novel machine learning methodologies to perform predictions of urban growth at regional levels by incorporating lead-lag non-linear relationships.
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Jun 12, 2024 · Here we develop a novel deep learning model to emulate subsurface flows simulated by the integrated ParFlow-CLM model across the contiguous US.
Nov 21, 2022 · This technical document summarizes recent advancements on spatio-temporal data-driven and machine learning methods for static and dynamic security assessment.
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This paper presents ST4ML, a distributed spatio-temporal data processing system to support scalable machine-learning-oriented applications. We propose a ...