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..
Stochastic Gradient Descent-Induced Drift of Representation in a Two-Layer Neural Network
Authors: Farhad Pashakhanloo, Alexei Koulakov
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Motivated by recent experimental findings of stimulus-dependent drift in the piriform cortex, we use theory and simulations to study this phenomenon in a two-layer linear feedforward network. |
| Researcher Affiliation | Academia | 1Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA. |
| Pseudocode | No | The paper contains mathematical derivations and theoretical models but no pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper describes generating synthetic data ('stimuli are drawn randomly and independently from a standard n-dimensional Gaussian distribution') but does not refer to a publicly available or open dataset with concrete access information. |
| Dataset Splits | No | The paper discusses numerical simulations and analytical derivations but does not specify dataset splits (e.g., training, validation, test percentages or counts) in the context of machine learning datasets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | In both plots m = n = 10, p = 20, γ = 0.04, and η = 0.005. (bottom) History of representations for three trial stimuli after 2.2 105 training steps. n = p = 3, γ = 0.1, η = 0.1, and α = 0.5. |