Benefits of Additive Noise in Composing Classes with Bounded Capacity
Authors: Alireza Fathollah Pour, Hassan Ashtiani
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 fathola@mcmaster.ca Hassan Ashtiani Department of Computing and Software Mc Master University zokaeiam@mcmaster.ca |
| 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. |