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..
Densely connected normalizing flows
Authors: Matej Grcić, Ivan Grubišić, Siniša Šegvić
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show significant improvements due to the proposed contributions and reveal state-of-the-art density estimation under moderate computing budgets. |
| Researcher Affiliation | Academia | Matej Grci c, Ivan Grubiši c and Siniša Šegvi c Faculty of Electrical Engineering and Computing University of Zagreb |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code available at: https://github.com/matejgrcic/Dense Flow |
| Open Datasets | Yes | We study the accuracy of density estimation on CIFAR-10 [35], Image Net [36] resized to 32 32 and 64 64 pixels and Celeb A [37]. |
| Dataset Splits | Yes | One MC sample is enough for accurate log-likelihood estimation since the per-example standard deviation is already about 0.01 bpd and a validation dataset size N additionally divides it by N. When compared with corresponding validation datasets, we achieve 37.1 on CIFAR10 and 38.5 on Image Net32. |
| Hardware Specification | Yes | Table 2: CIFAR-10 VFlow [24] 38M RTX 2080Ti 16 500 2.98 ... Dense Flow-74-10 130M RTX 3090 1 250 2.98. Image Net32 VFlow [24] 38M Tesla V100 16 1440 3.83. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | The first block of Dense Flow-74-10 uses 6 units with 5 glow-like modules in each Dense Flow unit, the second block uses 4 units with 6 modules, while the third block uses a single unit with 20 modules. We use the growth rate of 10 in all units. Each intra-module coupling starts with a projection to 48 channels. Subsequently, it includes a dense block with 7 densely connected layers, and the Nyström self-attention module with a single head. All models are trained on CIFAR-10 for 300 epochs and then fine-tuned for 10 epochs. We use the same training hyperparameters for all models. Training details are available in Appendix C. |