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..
Neural Guided Diffusion Bridges
Authors: Gefan Yang, Frank Van Der Meulen, Stefan Sommer
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the method through numerical experiments ranging from one-dimensional linear to high-dimensional nonlinear cases, offering qualitative and quantitative analyses. Section 5. Experiments. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Copenhagen, Universitetsparken 1, 2100 København, Denmark 2Department of Mathematics, Vrije Universiteit Amsterdam, De Boelelaan 1111, 1081HV Amsterdam, The Netherlands. |
| Pseudocode | Yes | Algorithm 1 Neural guided bridge training |
| Open Source Code | Yes | The codebase for reproducing all the experiments conducted in the paper is available in https://github.com/bookdiver/neuralbridge |
| Open Datasets | No | The paper's experiments use mathematical models (Linear Processes, Cell Diffusion Model, Fitz Hugh-Nagumo Model, Stochastic Landmark Matching) which generate data through simulation. The models' definitions and parameters are described or referenced in the paper, meaning there isn't a separate, pre-existing external dataset file requiring a specific link or repository for access, beyond the open-sourced code that generates the simulation data. |
| Dataset Splits | No | The paper's experiments involve simulating stochastic processes and generating trajectories (e.g., '25,000 independently sampled full trajectories'). It does not use pre-defined external datasets that would require training, validation, or test splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as exact GPU/CPU models, processor types, or memory amounts. |
| Software Dependencies | No | The paper mentions using JAX and Julia for implementation, but it does not specify version numbers for these software components or any other libraries that would be necessary for reproducibility. |
| Experiment Setup | Yes | The map ϑθ is modeled by a fully connected neural network with 3 hidden layers and 20 hidden dimensions for each layer. The model is trained with 25,000 independently sampled full trajectories of X . The batch size was taken to be N = 50 and the time step size δt = 0.002, leading to in total M = 500 time steps. The network was trained using the Adam (Kingma & Ba, 2017) optimizer with learning rate 0.001. |