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..
Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax
Authors: Andres Potapczynski, Gabriel Loaiza-Ganem, John P. Cunningham
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our construction enjoys theoretical advantages over the Gumbel Softmax, such as closed form KL, and significantly outperforms it in a variety of experiments. |
| Researcher Affiliation | Collaboration | Andres Potapczynski Zuckerman Institute Columbia University EMAIL Gabriel Loaiza-Ganem Layer 6 AI EMAIL John P. Cunningham Department of Statistics Columbia University EMAIL |
| Pseudocode | No | The paper does not contain any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/cunningham-lab/ igr. |
| Open Datasets | Yes | The datasets we use are handwritten digits from MNIST, fashion items from FMNIST and alphabet symbols from Omniglot. |
| Dataset Splits | Yes | We thus choose the temperature hyperparameter through cross validation, considering the range of possible temperatures {0.01, 0.03, 0.07, 0.1, 0.25, 0.4, 0.5, 0.67, 0.85, 1.0} and compare best-performing models. |
| Hardware Specification | No | No specific hardware details (like CPU/GPU models, memory) used for running the experiments are provided in the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | For the experiments involving a KL term, we use variational autoencoders (VAEs) [14]. We trained VAEs composed of 20 discrete variables with 10 categories each. For MNIST and Omniglot we used a fixed binarization and a Bernoulli decoder, whereas for FMNIST we use a Gaussian decoder. We ran each experiment 5 times and report averages plus/minus one standard deviation. We thus choose the temperature hyperparameter through cross validation, considering the range of possible temperatures {0.01, 0.03, 0.07, 0.1, 0.25, 0.4, 0.5, 0.67, 0.85, 1.0} and compare best-performing models. |