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..
Interacting Contour Stochastic Gradient Langevin Dynamics
Authors: Wei Deng, Siqi Liang, Botao Hao, Guang Lin, Faming Liang
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we compare the proposed algorithm with popular benchmark methods for posterior sampling. The numerical results show a great potential of ICSGLD for large-scale uncertainty estimation tasks. |
| Researcher Affiliation | Collaboration | Wei Deng1, 2, Siqi Liang1, Botao Hao3, Guang Lin1, Faming Liang1 1Purdue University 2Morgan Stanley 3Deep Mind |
| Pseudocode | Yes | Algorithm 1 Interacting contour stochastic gradient Langevin dynamics algorithm (ICSGLD). |
| Open Source Code | Yes | Code is available at github.com/Wayne DW/Interacting-Contour-Stochastic-Gradient-Langevin-Dynamics. |
| Open Datasets | Yes | Our proposed algorithm achieves appealing mode explorations using a fixed learning rate on the MNIST dataset... based on the UCI Mushroom data set... on CIFAR100, and report the test accuracy (ACC) and test negative log-likelihood (NLL) based on 5 trials with standard error. For the out-of-distribution prediction performance, we test the well-trained models in Brier scores (Brier) * on the Street View House Numbers dataset (SVHN). |
| Dataset Splits | No | The paper mentions training data and test data but does not explicitly provide details about specific training/validation/test dataset splits (e.g., percentages or exact counts for a validation set) within the main text or supplementary material sections provided. |
| Hardware Specification | No | The paper mentions distributed computing but does not provide specific hardware details such as GPU models, CPU models, or cloud instance types used for experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the experiments (e.g., 'Python 3.8' or 'PyTorch 1.9'). |
| Experiment Setup | Yes | The learning rate is fixed to 1e-6 and the temperature is set to 0.1. ...batch size of 2500... fix ζ = 3e4 and weight decay 25. ...choose 100,000 partitions and u = 10. The step size follows ωk = min{0.01, 1 k0.6+100}. ...initial learning rate is 2e-6... choose m = 200 and u = 200 for Res Net20, 32, and 56 and u = 60 for WRN-16-8. |