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 [1].
Riemannian Diffusion Models
Authors: Chin-Wei Huang, Milad Aghajohari, Joey Bose, Prakash Panangaden, Aaron C. Courville
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we demonstrate the expressive power of Riemannian diffusion models on a wide spectrum of smooth manifolds, such as spheres, tori, hyperboloids, and orthogonal groups. Our proposed method achieves new state-of-the-art likelihoods on all benchmarks. |
| Researcher Affiliation | Academia | University of Montreal & Mc Gill University & Mila EMAIL EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not explicitly state that source code is provided or link to a code repository. |
| Open Datasets | Yes | For spherical manifolds, we model the datasets compiled by Mathieu & Nickel (2020), which consist of earth and climate science events on the surface of the earth such as volcanoes (NGDC/WDS, 2022b), earthquakes (NGDC/WDS, 2022a), ๏ฌoods (Brakenridge, 2017), and ๏ฌres (EOSDIS, 2020). |
| Dataset Splits | No | The paper mentions 'different splits of the dataset' but does not provide specific percentages or counts for training, validation, and test sets, nor does it explicitly state the use of a validation set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments. |
| Software Dependencies | No | The paper mentions software like PyTorch and Matplotlib in its bibliography but does not provide specific version numbers for these or other software dependencies used in the experiments. |
| Experiment Setup | Yes | We report our detailed training procedure including selected hyperparameters for all models in D. |