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..
Benefits of Additive Noise in Composing Classes with Bounded Capacity
Authors: Alireza Fathollah Pour, Hassan Ashtiani
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Preliminary empirical results on MNIST dataset indicate that the amount of noise required to improve over existing uniform bounds can be numerically negligible (i.e., element-wise i.i.d. Gaussian noise with standard deviation 10 240).12 |
| Researcher Affiliation | Academia | Alireza Fathollah Pour Department of Computing and Software Mc Master University EMAIL Hassan Ashtiani Department of Computing and Software Mc Master University EMAIL |
| Pseudocode | No | The paper does not contain explicit pseudocode or algorithm blocks. It describes the methods and theoretical derivations in prose and mathematical notation. |
| Open Source Code | Yes | 2The source codes are available at https://github.com/fathollahpour/composition_noise |
| Open Datasets | Yes | We train fully connected neural networks on MNIST dataset. |
| Dataset Splits | No | The paper mentions using train and test data for the MNIST dataset but does not explicitly specify a validation split or its size/percentage for reproducibility. |
| Hardware Specification | Yes | The experiments were run on a single NVIDIA 2080 GPU. |
| Software Dependencies | No | Appendix I mentions 'Python and PyTorch' but does not specify their version numbers for reproducible software dependencies. |
| Experiment Setup | Yes | We use SGD with a learning rate of 0.01 and momentum 0.9. We train for 50 epochs. |