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..
Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities
Authors: Jonas Köhler, Leon Klein, Frank Noe
ICML 2020 | Venue PDF | 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. |