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..
Scaling Riemannian Diffusion Models
Authors: Aaron Lou, Minkai Xu, Adam Farris, Stefano Ermon
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically demonstrate that our improved Riemannian Diffusion Models improve performance and scale to high dimensional real-world tasks. For example, we can faithfully learn Wilson action on 4 4 SU(3) lattices (128 dimensions). Furthermore, when applied to contrastively learned hyperspherical embeddings (127 dimensions), our method enables better model interpretability by recovering the collapsed projection head representations. |
| Researcher Affiliation | Academia | Aaron Lou, Minkai Xu, Adam Farris, Stefano Ermon Stanford University EMAIL |
| Pseudocode | Yes | Algorithm 1: Heat Kernel Computation |
| Open Source Code | Yes | Code found at https://github.com/louaaron/Scaling-Riemannian-Diffusion |
| Open Datasets | Yes | We test on the compiled Earth science datasets from [38]... Table 2: We compare contrastive learning OOD detection methods on CIFAR-100. |
| Dataset Splits | No | The paper mentions using datasets for training and testing (e.g., 'We test on the compiled Earth science datasets from [38]', 'We train for 100000 gradient updates...'), but it does not provide specific details on train/validation/test splits (e.g., exact percentages, sample counts, or explicit references to predefined standard splits) that would be needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper mentions various components like 'Si LU activation function' and 'Adam optimizer' but does not specify software versions for any libraries or frameworks used (e.g., Python, PyTorch, TensorFlow, etc.). |
| Experiment Setup | Yes | We use a very similar architecture to the one used in Bortoli et al. [4] except we use the Si LU activation function without a learnable parameter [23] and a learning rate of 5 10 4. We use a standard MLP with 4 hidden layers and the Si LU activation function and learn with the Adam optimizer with learning rate set to 1e 3 [31]. ... We train for 100000 gradient updates with a batch size of 100. We train with a learning rate of 5 10 4 and perform 1000000 updates with a batch size of 512. |