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..
DIBS: Diversity Inducing Information Bottleneck in Model Ensembles
Authors: Samarth Sinha, Homanga Bharadhwaj, Anirudh Goyal, Hugo Larochelle, Animesh Garg, Florian Shkurti9666-9674
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on benchmark datasets: MNIST, CIFAR100, Tiny Image Net and MIT Places 2, and compared to the most competitive baselines show significant improvements in classification accuracy, under a shift in the data distribution and in out-of-distribution detection. |
| Researcher Affiliation | Collaboration | 1 University of Toronto 2 Vector Institute 3 Mila 4 Google Brain |
| Pseudocode | No | The paper describes mathematical formulations and algorithmic steps but does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We evaluate our method on benchmark datasets: MNIST, CIFAR100, Tiny Image Net and MIT Places 2, and compared to the most competitive baselines show significant improvements in classification accuracy, under a shift in the data distribution and in out-of-distribution detection. |
| Dataset Splits | No | The paper mentions training and test sets but does not explicitly provide details about a validation set or specific percentages for train/validation/test splits, nor does it refer to predefined splits with citations for all experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory, or processor types used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names like PyTorch or TensorFlow with their respective versions) needed to replicate the experiment. |
| Experiment Setup | Yes | For optimization, we use Stochastic Gradient Descent (SGD) (Bottou 2010) with a learning rate of 0.05 and momentum of 0.9 (Sutskever et al. 2013). We decay the learning rate by a factor of 10 every 30 epochs of training. |