Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks
Authors: Nanyang Ye, Zhanxing Zhu, Rafal Mantiuk
NeurIPS 2017 | Venue PDF | LLM Run Details | Input Tokens: 6,423 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,020 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our method to training deep neural networks and demonstrate its effectiveness on CIFAR-10 and CIFAR-100 datasets, achieving comparable test error to AdamW and SGD with momentum. |
| Researcher Affiliation | Academia | Department of Applied Mathematics, University of Washington |
| Pseudocode | Yes | Algorithm 1: Langevin Dynamics with Continuous Tempering (LDCT) |
| Open Source Code | No | The paper does not provide an explicit statement or a link to open-source code for the described methodology. |
| Open Datasets | Yes | We train a ResNet-18 model from scratch on CIFAR-10 dataset using an AdamW optimizer. The CIFAR-10 dataset is a widely used public benchmark dataset. |
| Dataset Splits | No | The paper states the dataset is split into "50,000 training images and 10,000 test images" but does not explicitly mention a validation split or its size. |
| Hardware Specification | Yes | All experiments are implemented in PyTorch and run on a single NVIDIA A100 GPU. |
| Software Dependencies | No | The paper states that experiments are "implemented in PyTorch" but does not specify the version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We train a ResNet-18 model from scratch on CIFAR-10 dataset using an AdamW optimizer with a learning rate set to 0.001 and a batch size of 128. Models are trained for 200 epochs with a weight decay of 0.05 and no learning rate scheduler. |