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 [1].
Statistical Efficiency of Score Matching: The View from Isoperimetry
Authors: Frederic Koehler, Alexander Heckett, Andrej Risteski
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 7, we perform several simulations which illustrate the close connection between isoperimetry and the performance of score matching. We give examples both when fitting the parameters of an exponential family and when the score function is fit using a neural network. |
| Researcher Affiliation | Academia | Frederic Koehler Stanford University EMAIL Alexander Heckett Carnegie Mellon University EMAIL Andrej Risteski Carnegie Mellon University EMAIL |
| Pseudocode | No | The paper does not contain any sections or blocks labeled 'Pseudocode' or 'Algorithm'. |
| Open Source Code | No | The paper does not provide an explicit statement about the release of its own source code, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper discusses synthetic or generative distributions (e.g., 'a bimodal distribution', 'a mixture of Gaussians') for simulations, but does not provide concrete access information (link, DOI, repository, or formal citation with authors/year) for a publicly available or open dataset. |
| Dataset Splits | No | The paper refers to 'training data' and 'finite number of samples n' but does not provide specific details on dataset splits (percentages, counts, or references to standard splits) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware (e.g., CPU, GPU models, memory, or cloud instances) used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names with versions) needed to replicate the experiments. |
| Experiment Setup | Yes | Model details: both models illustrated in the figure have 2048 tanh units and are trained via SGD on fresh samples for 300000 steps. |