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..
Normalizing flow neural networks by JKO scheme
Authors: Chen Xu, Xiuyuan Cheng, Yao Xie
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments with synthetic and real data show that the proposed JKO-i Flow network achieves competitive performance compared with existing flow and diffusion models at a significantly reduced computational and memory cost. |
| Researcher Affiliation | Academia | Chen Xu School of Industrial and Systems Engineering Georgia Tech Xiuyuan Cheng Department of Mathematics Duke University Yao Xie School of Industrial and Systems Engineering Georgia Tech |
| Pseudocode | Yes | Algorithm 1 Block-wise JKO-i Flow training |
| Open Source Code | Yes | Codes are available at https://github.com/hamrel-cxu/JKO-i Flow. |
| Open Datasets | Yes | The four high-dimensional real datasets (POWER, GAS, MINIBOONE, BSDS300) come from the University of California Irvine (UCI) machine learning data repository, and we follow the pre-processing procedures of [Papamakarios et al., 2017]. |
| Dataset Splits | No | POWER: 1.85M training sample and 205K test sample. GAS: 1M training sample, 100K test sample. MINIBOONE: 32K training sample, 3.6K test sample. BSDS300: 1.05M training sample, 250K test sample. (No explicit mention of validation set size or split.) |
| Hardware Specification | Yes | Specifically, on a single A100 GPU, our experiments took 24 hours on CIFAR10 and 30 hours on Imagenet-32. |
| Software Dependencies | No | All experiments are conducted using Py Torch [Paszke et al., 2019] and Py Torch Geometric [Fey and Lenssen, 2019]. (Specific version numbers for PyTorch and PyTorch Geometric are not stated.) |
| Experiment Setup | Yes | The initial schedule is by setting h0 = 0.75, ρ = 1.2. We set hmax = 5 for rose, checkerboard, and Olympic rings, and hmax = 3 for fractal tree. For reparametrization and refinement, we use η = 0.5 in the reparameterization iterations for all examples. The learning rate is 5e-3. On MNIST, we fix the batch size to be 2000 during training, and we train 15K batches per JKO-i Flow block. We fix the learning rate to be 1e-3. |