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..
Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks
Authors: Gauthier Gidel, Francis Bach, Simon Lacoste-Julien
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments4.1 Assump. 1 for Classification Datasets4.2 Linear AutoencoderIn Fig. 2, we plot the trace norm of W(t) and W(t)1W(t)2 as well as their respective reconstruction errors as a function of t the number of iterationsTable 1: Value of the quantities xy and x defined in (27). |
| Researcher Affiliation | Academia | Gauthier Gidel Mila & DIRO Universit e de Montr ealFrancis Bach INRIA & Ecole Normale Sup erieure PSL Research University, ParisSimon Lacoste-Julien Mila & DIRO Universit e de Montr eal |
| Pseudocode | No | No section or figure explicitly labeled 'Pseudocode' or 'Algorithm' was found. |
| Open Source Code | No | The paper does not provide any statement or link regarding the release of source code for the described methodology. |
| Open Datasets | Yes | MNIST [Le Cun et al., 2010], CIFAR-10 [Krizhevsky et al., 2014] and Image Net [Deng et al., 2009] |
| Dataset Splits | No | The paper mentions datasets used for experiments but does not provide specific details on training, validation, or test splits (e.g., percentages, sample counts, or cross-validation setup). |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for running experiments, such as exact GPU/CPU models or memory specifications. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names with versions). |
| Experiment Setup | Yes | In this experiment, we have p = d = 20, n = 1000, r = 5 and we generated synthetic data. First we generate a fixed matrix B Rd r such that, Bkl U([0, 1]), 1 k, l n. Then, for 1 i n, we sample xi Bzi + ϵi where zi N(0, D := diag(4, 2, 1, 1/2, 1/4)) and ϵi 10 3N(0, Id).If η < 1 2σ1 , η < 2 σi σi+1 σ2 i and η < σi σi+1 σ2 i+1 , for 1 i rxy 1.we initialize with W1(0) = U diag(e δ1, . . . , e δp)Q and W2(0) = Q 1 diag(e δ1, . . . , e δd)V |