Exchangeable Neural ODE for Set Modeling
Authors: Yang Li, Haidong Yi, Christopher Bender, Siyuan Shan, Junier B. Oliva
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the efficacy of our method over strong baselines. ... 4 Experiment The experiments are divided into three parts. First, we evaluate Ex NODE on point cloud classification (Sec. 4.2). Second, we conduct experiments to validate the efficacy of Ex NODE for point cloud generation and likelihood estimation (Sec. 4.3). Finally, we explore the temporal set modeling task (Sec. 4.4)... Table 1: Test Accuracy for point cloud classification... Table 2: Per Point Log Likelihood (PPLL) on test set. |
| Researcher Affiliation | Academia | Yang Li Department of Computer Science University of North Carolina at Chapel Hill yangli95@cs.unc.edu Haidong Yi Department of Computer Science University of North Carolina at Chapel Hill haidyi@cs.unc.edu Christopher M. Bender Department of Computer Science University of North Carolina at Chapel Hill bender@cs.unc.edu Siyuan Shan Department of Computer Science University of North Carolina at Chapel Hill siyuanshan@cs.unc.edu Junier B. Oliva Department of Computer Science University of North Carolina at Chapel Hill joliva@cs.unc.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We post our code at https://github.com/lupalab/Ex NODE. |
| Open Datasets | Yes | We evaluate Ex NODE on point cloud classification using Model Net40 [26]... we conduct experiments for set generative task using Spatial MNIST [3] and Model Net40 [3]. |
| Dataset Splits | No | The paper mentions training and testing but does not provide specific details about a validation dataset split. |
| Hardware Specification | Yes | The generative flow models could take roughly 4 days on one TITAN XP GPU |
| Software Dependencies | No | Our implementation of neural ODE utilizes the official implementation of the NODE [6]. We use dopri5 for flow models and RK4 for classification models. |
| Experiment Setup | No | Architectural details are provided in Appendix D. |