# A resource-efficient model for deep kernel learning

arXiv: https://arxiv.org/abs/2410.09926 · HTML: https://mlsanity.com/paper/2410.09926/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 46 references that are in the map.
- First posted on arXiv 13 Oct 2024.

## Builds on (most cited first, 12 of 12)

- [Deep Kernel Learning](https://mlsanity.com/paper/1511.02222/) (2015)
- [Caffe: Convolutional Architecture for Fast Feature Embedding](https://mlsanity.com/paper/1408.5093/) (2014)
- [MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems](https://mlsanity.com/paper/1512.01274/) (2015)
- [Mesh-TensorFlow: Deep Learning for Supercomputers](https://mlsanity.com/paper/1811.02084/) (2018)
- [Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis](https://mlsanity.com/paper/1802.09941/) (2018)
- [Theoretical Issues in Deep Networks: Approximation, Optimization and Generalization](https://mlsanity.com/paper/1908.09375/) (2019)
- [Scalable Deep Learning on Distributed Infrastructures: Challenges, Techniques and Tools](https://mlsanity.com/paper/1903.11314/) (2019)
- [A representer theorem for deep neural networks](https://mlsanity.com/paper/1802.09210/) (2018)
- [A representer theorem for deep kernel learning](https://mlsanity.com/paper/1709.10441/) (2017)
- [Deep Convolutional Networks are Hierarchical Kernel Machines](https://mlsanity.com/paper/1508.01084/) (2015)
- [Resource-Efficient Deep Learning: A Survey on Model-, Arithmetic-, and Implementation-Level Techniques](https://mlsanity.com/paper/2112.15131/) (2021)
- [The TensorFlow Partitioning and Scheduling Problem: It's the Critical Path!](https://mlsanity.com/paper/1711.01912/) (2017)

## Abstract (arXiv)

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.