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..
Traditional and Heavy Tailed Self Regularization in Neural Network Models
Authors: Michael Mahoney, Charles Martin
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Main Empirical Results. Our main empirical results consist in evaluating empirically the ESDs (and related RMT-based statistics) for weight matrices for a suite of DNN models, thereby probing the Energy Landscapes of these DNNs. |
| Researcher Affiliation | Collaboration | 1Calculation Consulting, 8 Locksley Ave, 6B, San Francisco, CA 94122 2ICSI and Department of Statistics, University of California at Berkeley, Berkeley, CA 94720. Correspondence to: Charles H. Martin <charles@Calculation Consulting.com>, Michael W. Mahoney <EMAIL>. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper links to a third-party GitHub repository (https://github.com/deepmind/sonnet/blob/master/sonnet/python/modules/nets/alexnet.py) for the Mini Alex Net architecture, but there is no explicit statement by the authors that they are releasing their own source code for the methodology described in this paper. |
| Open Datasets | Yes | We used Keras 2.0, using 20 epochs of the Ada Delta optimizer, on the MNIST data set. |
| Dataset Splits | No | The paper mentions '100.00% training accuracy, and 99.25% test accuracy on the default MNIST split' and 'Training and Test Accuracies' for Mini Alex Net. While training and test splits are implied, specific details about a validation split (percentages, counts, or explicit mention of a validation set) are not provided. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | Yes | We used Keras 2.0, with Tensor Flow as a backend. |
| Experiment Setup | Yes | All models are trained using Keras 2.x, with Tensor Flow as a backend. We use SGD with momentum, with a learning rate of 0.01, a momentum parameter of 0.9, and a baseline batch size of 32; and we train up to 100 epochs. |