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 [1].
A Langevin-like Sampler for Discrete Distributions
Authors: Ruqi Zhang, Xingchao Liu, Qiang Liu
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide extensive experimental results, including Ising models, restricted Boltzmann machines, deep energy-based models, binary Bayesian neural networks and text generation, to demonstrate the superiority of DLP in general settings. |
| Researcher Affiliation | Academia | 1The University of Texas at Austin. Correspondence to: Ruqi Zhang <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Samplers with Discrete Langevin Proposal (DULA and DMALA). |
| Open Source Code | Yes | We released the code at https://github. com/ruqizhang/discrete-langevin. |
| Open Datasets | Yes | Following Grathwohl et al. (2021), we train {W, a, b} with contrastive divergence (Hinton, 2002) on the MNIST dataset for one epoch. and We conduct regression on four UCI datasets (Dua & Graff, 2017). |
| Dataset Splits | Yes | The reported results are from the model which performs the best on the validation set. |
| Hardware Specification | No | The paper mentions that computations can be done "in parallel on CPUs and GPUs" but does not provide specific hardware details such as GPU/CPU models or memory specifications used for experiments. |
| Software Dependencies | No | The paper states "All methods are implemented in Pytorch" but does not provide specific version numbers for PyTorch or any other software dependencies needed for replication. |
| Experiment Setup | Yes | DMALA and DULA use a stepsize α of 0.4 and 0.2 respectively. and We set the batch size to 100. The stepsize α is set to be 0.1 for DULA and 0.2 for DMALA, respectively. The model is optimized by an Adam optimizer with a learning rate of 0.001. |