Position: $C^*$-Algebraic Machine Learning $-$ Moving in a New Direction
Authors: Yuka Hashimoto, Masahiro Ikeda, Hachem Kadri
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose a new direction for machine learning research: C -algebraic ML a cross-fertilization between C -algebra and machine learning. The mathematical concept of C -algebra is a natural generalization of the space of complex numbers. It enables us to unify existing learning strategies, and construct a new framework for more diverse and information-rich data models. We explain why and how to use C -algebras in machine learning, and provide technical considerations that go into the design of C -algebraic learning models in the contexts of kernel methods and neural networks. Furthermore, we discuss open questions and challenges in C -algebraic ML and give our thoughts for future development and applications. |
| Researcher Affiliation | Collaboration | 1NTT corporation, Tokyo, Japan 2Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan 3Keio University, Yokohama, Japan 4Aix-Marseille University, CNRS, LIS, Marseille, France. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states: 'Although source codes are provided by the authors of the papers, as far as we know, no software for C -algebraic machine learning has been developed so far.' This refers to source codes provided by authors of *other* papers cited, not the current paper providing its own source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve empirical experiments with datasets; therefore, no public or open dataset information is provided for training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets; therefore, no dataset split information for validation is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any empirical experiments; therefore, no hardware specifications for running experiments are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any empirical experiments; therefore, no specific software dependencies with version numbers are listed. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experiments; therefore, no experimental setup details like hyperparameters or training settings are provided. |