Coupled Gradient Estimators for Discrete Latent Variables
Authors: Zhe Dong, Andriy Mnih, George Tucker
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In systematic experiments, we show that our proposed categorical gradient estimators provide state-of-the-art performance, whereas even with additional Rao-Blackwellization, previous estimators (Yin et al., 2019) underperform a simpler REINFORCE with a leave-one-out-baseline estimator (Kool et al., 2019). |
| Researcher Affiliation | Industry | Zhe Dong Google Research, Brain Team zhedong@google.com Andriy Mnih Deep Mind andriy@deepmind.com George Tucker Google Research, Brain Team gjt@google.com |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and additional information: https://sites.google.com/view/disarm-estimator. |
| Open Datasets | Yes | on three datasets: binarized MNIST (Le Cun et al., 2010), Fashion MNIST (Xiao et al., 2017), and Omniglot (Lake et al., 2015). |
| Dataset Splits | Yes | We use the standard split into train, validation, and test sets. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions TensorFlow Probability without a version number and does not list other specific software dependencies with version numbers. |
| Experiment Setup | Yes | For most experiments, we used 32 latent variables with 64 categories unless speciļ¬ed otherwise. See Appendix A.2 for more details. |