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 embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Mean-field Underdamped Langevin Dynamics and its Spacetime Discretization
Authors: Qiang Fu, Ashia Camage Wilson
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method N-ULA in training a two-layer mean-field neural network to approximate a Gaussian function... Codes of our experiments are available at https://github.com/Qiang Fu09/NULA. More details of the experimental settings and discussion are postponed to Section F. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Yale University, New Haven, CT, USA 2Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA. |
| Pseudocode | Yes | Algorithm 1 (N-ULA) Require: F satisfies Assumptions 2.3-2.7 and 2.9 |
| Open Source Code | Yes | Codes of our experiments are available at https://github.com/Qiang Fu09/NULA. |
| Open Datasets | No | The paper mentions using 'dataset (ai, bi)n i=1' or 'randomly generated data samples' in its examples and experiments, but does not specify a publicly available dataset with a link, DOI, or formal citation. |
| Dataset Splits | No | The paper describes using 'n randomly generated data samples' but does not provide specific details on training, validation, or test splits (e.g., percentages, sample counts, or a description of the splitting methodology). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU or CPU models, or memory specifications. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | We give the actual updates of the methods involved in our experiment and provide the precise value of parameters in Table 2... We choose K = 104 and also fine-tune h1, λ1 and h2, λ2 to make fair comparison. We postpone our choice of hyperparameters to the Appendix F.1. For each algorithm in our experiment, we initialize xj 0 N(0, 10 2Id) and vj 0 N(0, 10 2Id) for j = 1, ..., N |