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..
Path-Gradient Estimators for Continuous Normalizing Flows
Authors: Lorenz Vaitl, Kim Andrea Nicoli, Shinichi Nakajima, Pan Kessel
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate for two application domains, i.e. VAEs and lattice field theories in theoretical physics, that a simple replacement of the standard total gradient by the path-gradient estimator improves the performance across different architectures of continuous normalizing flows, datasets as well as for fixed and adaptive step-size ODE solvers. |
| Researcher Affiliation | Academia | 1Machine Learning Group, Department of Electrical Engineering & Computer Science, Technische Universit at Berlin, Germany 2BIFOLD Berlin Institute for the Foundations of Learning and Data, Technische Universit at Berlin, Berlin, Germany 3RIKEN Center for AIP, 103-0027 Tokyo, Chuo City, Japan. |
| Pseudocode | Yes | Algorithm 1 Forw-Aug: Forward-mode derivative for path-wise gradient estimators for CNFs (...) Algorithm 2 Full path gradient computation (...) |
| Open Source Code | Yes | We provide code to reproduce the experiments using VAEs1. 1https://github.com/lenz3000/ffjord-path |
| Open Datasets | Yes | We repeat the VAE experiments in Grathwohl et al. (2019) which train a VAE for four datasets using a FFJORD flow. (...) MNIST, OMNIGLOT, CALTECH SILHOUETTES, FREY FACES |
| Dataset Splits | No | The training also used early-stopping which necessarily implies that training has no fixed runtime. |
| Hardware Specification | Yes | Each model was trained on a single A100 GPU. |
| Software Dependencies | No | The ODE solver was Dopri5. |
| Experiment Setup | Yes | Training was done with a learning rate of .001, the Adam optimizer (Kingma & Ba, 2015), batch-size 100. The ODE solver was Dopri5. |