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..
Quasi-Monte Carlo for 3D Sliced Wasserstein
Authors: Khai Nguyen, Nicola Bariletto, Nhat Ho
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conduct experiments on various 3D tasks, such as point-cloud comparison, point-cloud interpolation, image style transfer, and training deep point-cloud autoencoders, to demonstrate the favorable performance of the proposed QSW and RQSW variants1. |
| Researcher Affiliation | Academia | Khai Nguyen, Nicola Bariletto & Nhat Ho Department of Statistics and Data Sciences The University of Texas at Austin Austin, TX 78712, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 Monte Carlo estimation of the Sliced Wasserstein distance. Algorithm 2 Quasi-Monte Carlo approximation of the sliced Wasserstein distance. Algorithm 3 Randomized Quasi-Monte Carlo estimation of the Sliced Wasserstein distance with scrambling. Algorithm 4 The Randomized Quasi-Monte Carlo estimation of sliced Wasserstein distance with random rotation. |
| Open Source Code | Yes | 1Code for the paper is published at https://github.com/khainb/Quasi-SW. |
| Open Datasets | Yes | We select randomly four point-clouds (1, 2, 3, and 4 with 3 dimensions, 2048 points) from Shape Net Core-55 dataset (Chang et al., 2015) as shown in Figure 1. |
| Dataset Splits | No | The paper discusses training and testing, and mentions L=100 as the number of projections, but does not provide specific details on how the dataset was split into train/validation/test sets by percentages or counts. |
| Hardware Specification | Yes | We use a single NVIDIA V100 GPU to conduct experiments on training deep point-cloud autoencoder. Other applications are done on a desktop with an Intel core I5 CPU chip. |
| Software Dependencies | No | The paper mentions "POT library, Flamary et al. (2021)" but does not specify its version number. It also mentions "Point-Net Qi et al. (2017) architecture" but no software versions are provided for this or other dependencies. |
| Experiment Setup | Yes | We aim to optimize the following objective minϕ,γ EX µ(X)[SWp(PX, Pgγ(fϕ(X)))], where µ(X) is our data distribution, fϕ and gψ are a deep encoder and a deep decoder with Point-Net Qi et al. (2017) architecture. To optimize the objective, we use conventional MC estimation, QSW, and RQSW to approximate the gradient ϕ and ψ. We then utilize the standard SGD optimizer to train the autoencoder (with an embedding size of 256) for 400 epochs with a learning rate of 1e-3, a batch size of 128, a momentum of 0.9, and a weight decay of 5e-4. |