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..
Gradient Boosted Normalizing Flows
Authors: Robert Giaquinto, Arindam Banerjee
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of this technique for density estimation and, by coupling GBNF with a variational autoencoder, generative modeling of images. Our results show that GBNFs outperform their non-boosted analog, and, in some cases, produce better results with smaller, simpler flows. 5 Experiments: To evaluate GBNF, we highlight results on two toy problems, density estimation on real data, and boosted flows within a VAE for generative modeling of images. We boost coupling flows [20, 46] parameterized by feed-forward networks with Tan H activations and a single hidden layer. |
| Researcher Affiliation | Academia | Robert Giaquinto Arindam Banerjee Department of Computer Science & Engineering University of Minnesota, Twin Cities Minneapolis, MN 55455, USA |
| Pseudocode | No | The paper does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the described methodology. |
| Open Datasets | Yes | We report density estimation results on the POWER, GAS, HEPMASS, and MINIBOONE datasets from the UCI machine learning repository [23], as well as the BSDS300 dataset [55]. We compare our model on the same image datasets as those used in van den Berg et al. [78]: Freyfaces, Caltech 101 Silhouettes [54], Omniglot [49], and statically binarized MNIST [50]. |
| Dataset Splits | No | The paper refers to using datasets and evaluating performance (e.g., test loss in Figure 2, Table 1 and 2 reporting test set metrics), but it does not specify explicit training/validation/test splits with percentages or sample counts. It mentions |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running its experiments. It only mentions general computing support from the University of Minnesota Supercomputing Institute (MSI) in the Acknowledgements. |
| Software Dependencies | No | The paper mentions using |
| Experiment Setup | Yes | In the toy experiments flows are trained for 25k iterations using the Adam optimizer [45]. For all other experiments details on the datasets and hyperparameters can be found in Appendix A. In our experiments that augment the VAE with a GBF-based posterior, we find good results setting the regularization λ = 1.0. In the density estimation experiments, better results are often achieved with λ near 0.8. Further, we anneal the KL [6, 37, 72] in (14) cyclically [31], with restarts corresponding to the introduction of new boosting components |