E(n) Equivariant Normalizing Flows
Authors: Victor Garcia Satorras, Emiel Hoogeboom, Fabian Fuchs, Ingmar Posner, Max Welling
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments Results: In Table 1 we report the cross-validated Negative Log Likelihood for the test partition. |
| Researcher Affiliation | Academia | Uv A-Bosch Delta Lab, University of Amsterdam1, Department of Engineering Science, University of Oxford2 |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link regarding the open-sourcing of the code for the methodology described. |
| Open Datasets | Yes | QM9 (Ramakrishnan et al., 2014) is a molecular dataset standarized in machine learning as a chemical property prediction benchmark. |
| Dataset Splits | Yes | For both datasets we use 1,000 validation samples, and 1,000 test samples. |
| Hardware Specification | No | The paper does not specify the exact hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using the 'torchdiffeq package' and 'rdkit toolkit' but does not specify their version numbers or any other software dependencies with versions. |
| Experiment Setup | Yes | Our E-NF method and its non-equivariant variants (GNF, GNF-att, GNF-att-aug) consist of 3 layers each, 32 features per layer, and Si LU activation functions. |