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..
Self-Attention Amortized Distributional Projection Optimization for Sliced Wasserstein Point-Cloud Reconstruction
Authors: Khai Nguyen, Dang Nguyen, Nhat Ho
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To verify the effectiveness of our proposal, we evaluate our methods on the point-cloud reconstruction task and its two downstream tasks including transfer learning and point-cloud generation. The quantitative results are summarized in Table 1. |
| Researcher Affiliation | Collaboration | 1Department of Statistics and Data Sciences, University of Texas at Austin, USA 2Vin AI Research. |
| Pseudocode | Yes | Algorithm 1 Sampling from v MF distribution |
| Open Source Code | Yes | Code for the paper is published at https://github. com/hsgser/Self-Amortized-DSW. |
| Open Datasets | Yes | Our autoencoder is trained on the Shape Net Core-55 dataset (Chang et al., 2015) with a batch size of 128 and a point-cloud size of 2048. We train it for 300 epochs using an SGD optimizer with an initial learning rate of 1e-3, a momentum of 0.9, and a weight decay of 5e-4. |
| Dataset Splits | Yes | the chair category of Shape Net is divided into train/valid/test sets in an 85/5/10 ratio. |
| Hardware Specification | Yes | All experiments are run on NVIDIA V100 GPUs. |
| Software Dependencies | No | The paper mentions using an 'Adam optimizer (Kingma & Ba, 2014)' but does not provide specific version numbers for software dependencies like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | Our autoencoder is trained on the Shape Net Core-55 dataset (Chang et al., 2015) with a batch size of 128 and a point-cloud size of 2048. We train it for 300 epochs using an SGD optimizer with an initial learning rate of 1e-3, a momentum of 0.9, and a weight decay of 5e-4. |