Learning Invariances in Neural Networks from Training Data
Authors: Gregory Benton, Marc Finzi, Pavel Izmailov, Andrew G. Wilson
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For the experiments in this and all following sections we use a 13-layer CNN architecture from Laine and Aila [21].", "we achieve 98.9% test accuracy.", "Table 1: Test accuracy for models trained on CIFAR-10 with different augmentations applied to the training data.", "Table 2: Test MAE (in meV) on QM9 tasks trained with specified augmentation. |
| Researcher Affiliation | Academia | Gregory Benton Marc Finzi Pavel Izmailov Andrew Gordon Wilson Courant Institute of Mathematical Sciences New York University |
| Pseudocode | Yes | Algorithm 1: Learning Invariances with Augerino |
| Open Source Code | Yes | The accompanying code can be found at https://github.com/g-benton/learning-invariances. |
| Open Datasets | Yes | The rotated MNIST dataset (rot MNIST)", "Using the CIFAR-10 dataset", "We test out our method on the molecular property prediction dataset QM9 [3, 34]", "construct rot Cam Vid, a variation of the Cam Vid dataset [5, 4]", "learn a uniform distribution over the brightness and contrast adjustments on STL-10 [6]. |
| Dataset Splits | No | The paper mentions 'training set' and 'test set' for some datasets but does not explicitly provide specific train/validation/test percentages or counts needed for reproduction in the main text. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments are provided in the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names with their versions) that are needed to replicate the experiment. |
| Experiment Setup | Yes | For the experiments in this and all following sections we use a 13-layer CNN architecture from Laine and Aila [21].", "We compare Augerino trained with three values of λ from Equation 5; λ = {0.01, 0.05, 0.1}", "training for 200 epochs", "We train the models for 500 epochs on MAE. |