On Power Laws in Deep Ensembles
Authors: Ekaterina Lobacheva, Nadezhda Chirkova, Maxim Kodryan, Dmitry P. Vetrov
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct our experiments with convolutional neural networks, Wide Res Net [33] and VGG16 [27], on CIFAR-10 [16] and CIFAR-100 [17] datasets. [...] The empirical results presented in sections 4, 5, 6, 7 were supported by the Russian Science Foundation grant 19-71-30020. |
| Researcher Affiliation | Collaboration | 1Samsung-HSE Laboratory, National Research University Higher School of Economics 2Samsung AI Center Moscow Moscow, Russia {elobacheva,nchirkova,mkodryan,dvetrov}@hse.ru |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our source code is available at https://github.com/nadiinchi/power_laws_deep_ensembles. |
| Open Datasets | Yes | We conduct our experiments with convolutional neural networks, Wide Res Net [33] and VGG16 [27], on CIFAR-10 [16] and CIFAR-100 [17] datasets. |
| Dataset Splits | Yes | Following [2], we use the test-time cross-validation to compute the CNLL. [...] For each network size, we tune hyperparameters (weight decay and dropout) using grid search. |
| Hardware Specification | No | The paper mentions support from 'HPC facilities at NRU HSE' but does not provide specific details on CPU, GPU models, or other hardware specifications used for experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For each network size, we tune hyperparameters (weight decay and dropout) using grid search. We train all networks for 200 epochs with SGD with an annealing learning schedule and a batch size of 128. |