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].
Manifold Diffusion Fields
Authors: Ahmed A. A. Elhag, Yuyang Wang, Joshua M. Susskind, Miguel Ángel Bautista
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results on multiple datasets and manifolds including challenging scientific problems like weather prediction or molecular conformation show that MDF can capture distributions of such functions with better diversity and fidelity than previous approaches. |
| Researcher Affiliation | Industry | Ahmed A. Elhag , Yuyang Wang, Joshua M. Susskind, Miguel Angel Bautista Apple EMAIL |
| Pseudocode | Yes | Algorithm 1 Training; Algorithm 2 Sampling |
| Open Source Code | No | The paper mentions using a 'modified version of the publicly available repository is used for Perceiver IO 2.' and an 'ERA5 dataset... is available at' external links, but does not explicitly state that the code for MDF itself is open-sourced or provide a specific link for it. |
| Open Datasets | Yes | MNIST (Le Cun et al., 1998) and Celeb A-HQ (Karras et al., 2018) datasets, where images are texture mapped into the meshes using (Sullivan & Kaszynski, 2019), models are evaluated on the standard tests sets for these datasets. Following the standard setting for molecule conformer prediction we use the GEOM-QM9 dataset (Ruddigkeit et al., 2012; Ramakrishnan et al., 2014) which contains 130K molecules ranging from 10 to 40 atoms. |
| Dataset Splits | Yes | We use the same train/val/test splits as Torsional Diffusion (Jing et al., 2022) and use the same metrics to compare the generated and ground truth conformer ensembles: Average Minimum RMSD (AMR) and Coverage. |
| Hardware Specification | Yes | Each model was trained on an machine with 8 Nvidia A100 GPUs, we trained models for 3 days. |
| Software Dependencies | No | The paper mentions 'Perceiver IO' and 'Adam' optimizer, but does not provide specific version numbers for key software dependencies like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | Table 8: Hyperparameters and settings for MDF on different manifolds. (train res., #context set, #query set, #eigenfuncs (k), #freq pos. embed t, #latents, #dim latents, #blocks, #dec blocks, #self attends per block, #self attention heads, #cross attention heads, batch size, lr, epochs) |