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].
Path Auxiliary Proposal for MCMC in Discrete Space
Authors: Haoran Sun, Hanjun Dai, Wei Xia, Arun Ramamurthy
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we show that our path auxiliary algorithms considerably outperform other generic samplers on various discrete models for sampling, inference, and learning. Our method can also be used to train deep EBMs for high dimensional discrete data. |
| Researcher Affiliation | Collaboration | Haoran Sun Georgia Institute of Technology EMAIL Hanjun Dai Google Brain EMAIL Wei Xia Amazon EMAIL Arun Ramamurthy Siemens EMAIL |
| Pseudocode | Yes | Algorithm 1: Path Auxiliary Sampler (PAS) and the fast version (PAFS) |
| Open Source Code | Yes | The code can be found at https://github.com/ha0ransun/Path-Auxiliary-Sampler.git. |
| Open Datasets | Yes | We follow Grathwohl et al. (2021) to train a RBM with 500 hidden units on the MNIST dataset using contrastive divergence(Hinton, 2002). ... We train deep EBMs paramterized by Residual Networks(He et al., 2016) on small binary image datasets using PCD(Tieleman & Hinton, 2009) with a replay buffer(Du & Mordatch, 2019). ... Static MNIST, Dynamic MNIST, Omniglot, Caltech Silhouettes |
| Dataset Splits | No | The paper mentions tuning 'the expected path length in {3, 5, 7}, and report the result with the best validation likelihood' but does not specify details about the dataset split used for validation (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory) used to run the experiments. It mentions that 'the energy function evaluation and gradient calculation usually dominates the computation' but provides no hardware specifications. |
| Software Dependencies | No | The paper mentions that 'both of the methods implemented using pytorch' but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We use the same setting as Grathwohl et al. (2021), including the batch size, number of iterations, the PCD hyper parameters, etc. ... For our method, we also tune the expected path length in {3, 5, 7}. |