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].
A mathematical model for automatic differentiation in machine learning
Authors: Jérôme Bolte, Edouard Pauwels
NeurIPS 2020 | Venue PDF | LLM Run Details
| 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. |