Abstract
According to Hughes phenomenon, the major challenges encountered in computations with learning models come from the scale of complexity, e.g. the so-called curse of dimensionality. Approaches for accelerated learning computations range from model- to implementation-level. The first type is rarely used in its basic form. Perhaps, this is due to the theoretical understanding of mathematical insights. We describe a model-level decomposition approach that combines both the decomposition of the objective function and of data. We perform a feasibility analysis of the resulting algorithm, both in terms of accuracy and scalability.
Keywords
- Parallel machine learning
- parallel and distributed deep learning
- GPU parallelism
- domain decomposition
- problem and model reduction
MSC
68T07, 65K05
Cite this article
@article{cai-7011,
author = {Luisa D'Amore},
title = {Resource-Efficient Model for Deep Kernel Learning},
journal = {Computing and Informatics},
volume = {44},
number = {1},
pages = {1--25},
year = {2025},
issn = {1335-9150},
doi = {10.31577/cai_2025_1_1}
}
TY - JOUR AU - Luisa D'Amore TI - Resource-Efficient Model for Deep Kernel Learning JO - Computing and Informatics VL - 44 IS - 1 SP - 1 EP - 25 PY - 2025 SN - 1335-9150 DO - 10.31577/cai_2025_1_1 UR - https://doi.org/10.31577/cai_2025_1_1 KW - Parallel machine learning KW - parallel and distributed deep learning KW - GPU parallelism KW - domain decomposition KW - problem and model reduction ER -
D'Amore, L. (2025). Resource-Efficient Model for Deep Kernel Learning. Computing and Informatics, 44(1), 1–25. https://doi.org/10.31577/cai_2025_1_1