Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities
Authors: Jonas Köhler, Leon Klein, Frank Noe
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using benchmark systems motivated from molecular physics, we demonstrate that those symmetry preserving flows can provide better generalization capabilities and sampling efficiency. |
| Researcher Affiliation | Academia | 1Freie Universit at Berlin, Department of Mathematics and Computer Science. 2Freie Universit at Berlin, Department of Physics. 3Rice University, Department of Chemistry. |
| Pseudocode | No | The paper describes methods and equations, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about making its source code publicly available or include links to a code repository. |
| Open Datasets | No | The training data is generated by taking 10 / 100 / 1, 000 / 10, 000 samples from a long MCMC trajectory (throwing away 1, 000 burn-in samples to enforce equilibration). The paper defines the potential functions for the systems but does not provide concrete access information (link, DOI, etc.) for a publicly available dataset used for training. |
| Dataset Splits | No | The paper mentions generating training data and evaluating on an 'independent 10,000 trajectory' for testing, but does not specify a separate validation split for hyperparameter tuning. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions software like 'Adam' and 'dopri5' and 'Runge-Kutta' as solvers, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We compare the OTD implementation presented in (Chen et al., 2018; Grathwohl et al., 2018) using the dopri5-option (atol = 10 10, rtol = 10 5) to the DTO implementation given by Gholami et al. using a fixed grid of 20 steps and 4th-order Runge-Kutta as solver. ... We train both flows using Adam with weight decay (Kingma & Ba, 2014; Loshchilov & Hutter) until convergence. |