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..
Feature learning via mean-field Langevin dynamics: classifying sparse parities and beyond
Authors: Taiji Suzuki, Denny Wu, Kazusato Oko, Atsushi Nitanda
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Numerical Experiment We validate our theoretical results by numerical experiment on synthetic data. Specifically, we consider the classification of 2-sparse parity with varying dimensionality d and sample size n. ... Figure 1 shows the average test accuracy over five trials. |
| Researcher Affiliation | Academia | Taiji Suzuki1,2, Denny Wu3,4, Kazusato Oko1,2, Atsushi Nitanda2,5 1University of Tokyo, 2RIKEN AIP, 3New York University, 4Flatiron Institute, 5Kyushu Institute of Technology |
| Pseudocode | No | The paper describes processes using mathematical equations like “Xi τ+1 = Xi τ η δF(µτ) δµ (Xi τ) + p 2ληξi τ, (4)”, but it does not contain a formal pseudocode or algorithm block. |
| Open Source Code | No | The paper does not contain any statement about releasing source code or provide links to a code repository. |
| Open Datasets | No | Recall that the samples {(zi, yi)}n i=1 are independently generated so that zi follows the uniform distribution on { 1/ d}d and yi = dζi,1ζi,2 { 1} (zi = (ζi,1, . . . , ζi,d)). |
| Dataset Splits | No | The paper mentions using “sample size n” for training data and reports “test accuracy”, but it does not specify train/validation/test splits or cross-validation. |
| Hardware Specification | No | The paper does not specify any hardware details used for running experiments. |
| Software Dependencies | No | The paper mentions “The logistic loss is used for the training objective.” but does not list any software dependencies with specific version numbers. |
| Experiment Setup | Yes | A finite-width approximation of the mean-field neural network 1 N PN j=1 hxj(z) is employed with the width N = 2, 000. ... and the scaling parameter R is set to 15. We trained the network using noisy gradient descent with η = 0.2, λ1 = 0.1, and λ = 0.1/d (fixed during the whole training) until T = 10, 000. |