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..
Random Fully Connected Neural Networks as Perturbatively Solvable Hierarchies
Authors: Boris Hanin
JMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the distribution of fully connected neural networks with Gaussian random weights/biases and L hidden layers, each of width proportional to a large parameter n. For polynomially bounded non-linearities we give sharp estimates in powers of 1/n for the joint cumulants of the network output and its derivatives. We further show that network cumulants form a perturbatively solvable hierarchy in powers of 1/n. |
| Researcher Affiliation | Academia | Boris Hanin EMAIL Department of Operations Research and Financial Engineering Princeton University Princeton, NJ 08544, USA |
| Pseudocode | No | The paper contains mathematical derivations, theorems, and proofs, but no structured pseudocode or algorithm blocks are present. |
| Open Source Code | No | The paper does not provide explicit statements about releasing source code for the methodology or links to code repositories. The provided link (http://jmlr.org/papers/v25/23-0643.html) is for attribution requirements of the paper's license, not for source code. |
| Open Datasets | No | This paper is theoretical and does not conduct experiments on datasets. While it mentions applications of neural networks in general (e.g., 'self-driving cars (Krizhevsky et al. (2012))'), it does not specify or provide access information for any datasets used in its own research. |
| Dataset Splits | No | This paper is theoretical and does not involve experiments using datasets, therefore, no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental procedures or hardware used for computations. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical derivations, thus it does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper focuses on theoretical analysis and mathematical proofs of neural network properties. It does not describe an experimental setup, hyperparameters, or training configurations. |