Neural Oscillators are Universal
Authors: Samuel Lanthaler, T. Konstantin Rusch, Siddhartha Mishra
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove that neural oscillators are universal, i.e, they can approximate any continuous and casual operator mapping between time-varying functions, to desired accuracy. This universality result provides theoretical justification for the use of oscillator based ML systems. The proof builds on a fundamental result of independent interest, which shows that a combination of forced harmonic oscillators with a nonlinear read-out suffices to approximate the underlying operators. |
| Researcher Affiliation | Academia | Samuel Lanthaler California Institute of Technology slanth@caltech.edu T. Konstantin Rusch ETH Zurich Siddhartha Mishra ETH Zurich |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper discusses various architectures and models but does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | No | The paper is theoretical and does not involve training on datasets; it references existing datasets (e.g., Fashion-MNIST, MNIST) as examples of benchmarks where oscillatory systems have been used, but not for its own direct experimentation. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments with dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify software dependencies with version numbers for experimental replication. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. |