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].
Differential Equation Scaling Limits of Shaped and Unshaped Neural Networks
Authors: Mufan Bill Li, Mihai Nica
TMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Figure 1: Empirical distribution of the transformed correlation rt = log(ℓ2(1 − ρℓ)) for an unshaped ReLU MLP, SDE sample density computed via kernel density estimation. Simulated with n = d = 150, ρ0 = 0.3, r0 = log(0.7), SDE step size 10−2, and 213 samples. |
| Researcher Affiliation | Academia | Mufan (Bill) Li EMAIL Princeton University Mihai Nica EMAIL University of Guelph and Vector Institute |
| Pseudocode | No | The paper describes mathematical formulations and proofs but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about the release of source code, nor does it include links to a code repository. |
| Open Datasets | No | The paper does not mention the use of any external, publicly available datasets. The data for Figure 1 appears to be generated through simulation rather than loaded from a specific dataset. |
| Dataset Splits | No | The paper does not utilize external datasets that would require explicit training/test/validation splits. The presented results are based on mathematical derivations and a simulation without such data partitioning. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to perform the simulations or computations. |
| Software Dependencies | No | The paper mentions 'Sym Py (Meurer et al., 2017)' for Taylor expansion in the proof of Theorem 4.1, but it does not specify a version number for SymPy or any other software dependencies crucial for replicating the experiments. |
| Experiment Setup | Yes | The simulation shown in Figure 1 specifies concrete parameters: 'Simulated with n = d = 150, ρ0 = 0.3, r0 = log(0.7), SDE step size 10−2, and 213 samples.' |