提出一种高效深度核学习模型，降低高维计算复杂度。
A resource-efficient model for deep kernel learning
- 从模型层面分解算子与网络结构，提升效率
- 验证了算法在准确性和可扩展性上的可行性
- 适合高维数据处理与资源受限场景
根据休斯现象，学习模型计算中的主要挑战源于复杂度规模，例如所谓的维度诅咒。目前存在多种加速学习计算且保持精度的方法，涵盖模型级到实现级的不同策略。据我们所知，模型级方法尚未以基础形式广泛应用，可能由于对模型分解的数学机理理解不足，导致理论改进能力滞后。本文提出一种模型级分解方法，同时对算子和网络进行分解，并对所得算法在准确性与可扩展性方面进行了可行性分析。
原文摘要 · Abstract (English)
According to the Hughes phenomenon, the major challenges encountered in computations with learning models comes from the scale of complexity, e.g. the so-called curse of dimensionality. There are various approaches for accelerate learning computations with minimal loss of accuracy. These approaches range from model-level to implementation-level approaches. To the best of our knowledge, the first one is rarely used in its basic form. Perhaps, this is due to theoretical understanding of mathematical insights of model decomposition approaches, and thus the ability of developing mathematical improvements has lagged behind. We describe a model-level decomposition approach that combines both the decomposition of the operators and the decomposition of the network. We perform a feasibility analysis on the resulting algorithm, both in terms of its accuracy and scalability.
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