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..
Saddle-to-Saddle Dynamics in Diagonal Linear Networks
Authors: Scott Pesme, Nicolas Flammarion
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide numerical experiments to support our findings. |
| Researcher Affiliation | Academia | Scott Pesme EPFL EMAIL Flammarion EPFL EMAIL |
| Pseudocode | Yes | Algorithm 1: Successive saddles and jump times of limα 0 βα |
| Open Source Code | No | The paper does not provide any explicit statements about releasing open-source code or links to a code repository. |
| Open Datasets | No | For each experiment we generate our dataset as yi = xi, β where xi = N(0, H) for a a diagonal covariance matrix H and β is a vector of Rd. The only assumption we make on the data throughout the paper is that the inputs (x1, . . . , xn) are in general position. |
| Dataset Splits | No | The paper describes how the data is generated for each experiment, but does not specify training, validation, or test splits. It directly uses the generated data for the numerical experiments. |
| Hardware Specification | No | The paper describes the experimental setup, but does not specify any hardware details (e.g., CPU, GPU, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions that "Gradient descent is run with a small step size" but does not provide specific software names with version numbers for reproducibility. |
| Experiment Setup | Yes | For each experiment we generate our dataset as yi = xi, β where xi = N(0, H) for a a diagonal covariance matrix H and β is a vector of Rd. Gradient descent is run with a small step size and from initialisation u0 = √2α1d and v0 = 0d for some initialisation scale α > 0. Figure 1 and Figure 4 (Left): (n, d, α) = (5, 7, 10−120), H = Id, β = (10, 20, 0, 0, 0, 0, 0) ∈ R7. |