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..
Deep Density Destructors
Authors: David Inouye, Pradeep Ravikumar
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate our framework on a 2D dataset, MNIST, and CIFAR-10. |
| Researcher Affiliation | Academia | 1Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA. Correspondence to: David I. Inouye <EMAIL>. |
| Pseudocode | No | The paper describes algorithms (e.g., a greedy training algorithm) but does not present them in a structured pseudocode or algorithm block format. |
| Open Source Code | Yes | Code is available on first author s website. |
| Open Datasets | Yes | We now give some initial results using the MNIST and CIFAR-10 datasets to show that it is possible to train density destructor models on larger datasets. We base our experiments on the unconditional (i.e. unsupervised) MNIST and CIFAR-10 experiments in (Papamakarios et al., 2017) and use the same preprocessed data as in (Papamakarios et al., 2017). |
| Dataset Splits | Yes | We implement a simple non-parametric greedy algorithm which merely estimates a density at each layer, transforms the training data via the associated destructor and repeats this process until the likelihood on a held-out validation set decreases. |
| Hardware Specification | Yes | Note that the timings for the baselines from (Papamakarios et al., 2017) are based on using a Titan X GPU whereas our methods merely use at most 10 CPUs. |
| Software Dependencies | No | The paper mentions using "Python sci-kit learn library (Pedregosa et al., 2011) and mlpack (Curtin et al., 2013)" but does not provide specific version numbers for these libraries. |
| Experiment Setup | Yes | We build a canonical destructor by composing the independent standard normal inverse CDF, a random linear projection, a standard normal CDF (which returns the values to the unit hypercube) and finally an independent histogram on the unit hypercube: Fhist(Φ(ArandΦ 1(x))). The histogram was estimated with 20 bins and a regularization parameter (pseudo-counts) α in the set {0.1, 1, 10}. |