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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
A mathematical model for automatic differentiation in machine learning
Authors: Jérôme Bolte, Edouard Pauwels
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 15,738 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,905 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | A mathematical model for automatic differentiation in machine learning, we provide a simple class of functions, a nonsmooth calculus, and show how they apply to stochastic approximation methods. Theorem 1 (Algorithmic differentiation does not induce an operator on functions), Theorem 2 (Algorithmic differentiation outputs a selection gradient), Theorem 3, Theorem 4 (Convergence and insignificance of artefacts) |
| Researcher Affiliation | Academia | J erˆome Bolte Toulouse School of Economics Univ. Toulouse Toulouse, France Edouard Pauwels IRIT, CNRS Univ. Toulouse Toulouse, France |
| Pseudocode | Yes | Algorithm 1: Program evaluation, Algorithm 2: Algorithmic differentiation computes selection gradients |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating the availability of open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and focuses on mathematical models and proofs. It does not describe training on a specific dataset or provide access information for any dataset used in empirical studies. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments, thus no dataset validation split information is provided. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Tensor Flow' and 'Py Torch' as general implementations but does not provide specific version numbers for software dependencies relevant to replicating its work. |
| Experiment Setup | No | The paper is theoretical and does not describe specific experimental setup details such as hyperparameter values or training configurations. |