Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Neural Oscillators are Universal
Authors: Samuel Lanthaler, T. Konstantin Rusch, Siddhartha Mishra
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 24,217 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,512 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL 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. |