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..
Estimating Gradients for Discrete Random Variables by Sampling without Replacement
Authors: Wouter Kool, Herke van Hoof, Max Welling
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments with a toy problem, a categorical Variational Auto-Encoder and a structured prediction problem show that our estimator is the only estimator that is consistently among the best estimators in both high and low entropy settings. |
| Researcher Affiliation | Collaboration | Wouter Kool University of Amsterdam ORTEC EMAIL Herke van Hoof University of Amsterdam EMAIL Max Welling University of Amsterdam CIFAR EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1Code available at https://github.com/wouterkool/estimating-gradients-without-replacement. |
| Open Datasets | Yes | The dataset is MNIST, statically binarized by thresholding at 0.5 (although we include results using the standard binarized dataset by Salakhutdinov & Murray (2008); Larochelle & Murray (2011) in Section G.2). |
| Dataset Splits | Yes | Figure 5 shows the -ELBO evaluated during training on the validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models used for running its experiments. |
| Software Dependencies | No | The paper mentions 'PyTorch' and 'Adam' but does not specify version numbers for these or other key software components. |
| Experiment Setup | Yes | We optimize the ELBO using the analytic KL for 1000 epochs using the Adam (Kingma & Ba, 2015) optimizer. We use a learning rate of 10−3 for all estimators except Gumbel-Softmax and RELAX, which use a learning rate of 10−4 as we found they diverged with a higher learning rate. and We did not do any hyperparameter optimization and used the exact same training details, using the Adam optimizer (Kingma & Ba, 2015) with a learning rate of 10−4 (no decay) for 100 epochs for all estimators. For the baselines, we used the same batch size of 512, but for estimators that use k = 4 samples, we used a batch size of 512 / 4 = 128 to compensate for the additional samples. |