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..
Deep Generalized Schrödinger Bridge
Authors: Guan-Horng Liu, Tianrong Chen, Oswin So, Evangelos Theodorou
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our Deep GSB on two classes of MFGs, including classical crowd navigation (d=2) and high-dimensional (d=1000) opinion depolarization. For crowd navigation, we consider three MFGs appearing in prior methods [14, 15], including (i) asymmetric obstacle avoidance, (ii) entropy interaction with a V-shape bottleneck, and (iii) congestion interaction on an S-tunnel. ... We show that our proposed objective function provides necessary and sufficient conditions to the mean-field problem. Our method, named Deep Generalized Schrödinger Bridge (Deep GSB), not only outperforms prior methods in solving classical population navigation MFGs, but is also capable of solving 1000-dimensional opinion depolarization, setting a new state-of-the-art numerical solver for high-dimensional MFGs. |
| Researcher Affiliation | Academia | 1Georgia Institute of Technology, USA 2Massachusetts Institute of Technology, USA EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1 Deep Generalized Schrödinger Bridge (Deep GSB) |
| Open Source Code | Yes | Our code will be made available at https://github.com/ghliu/Deep GSB. |
| Open Datasets | No | The data is generated randomly at each training iteration. For crowd navigation, we consider three MFGs appearing in prior methods [14, 15], including (i) asymmetric obstacle avoidance, (ii) entropy interaction with a V-shape bottleneck, and (iii) congestion interaction on an S-shape tunnel. ... For opinion depolarization, we set ρ0 and ρtarget to two zero-mean Gaussians with varying variances for representing the initially polarized and desired moderated opinion distributions. |
| Dataset Splits | No | No explicit train/validation/test dataset splits are provided. The paper mentions data is 'generated randomly at each training iteration'. |
| Hardware Specification | Yes | All experiments were run on a single NVIDIA A100 GPU. |
| Software Dependencies | No | The code is written in PyTorch [71]. |
| Experiment Setup | Yes | All networks adopt sinusoidal time embeddings and are trained with Adam W [56]. All SDEs in (11, 12) are solved with the Euler-Maruyama method. ... we leave the discussion of critic parametrization Deep GSB-c, along with additional experimental details, to Appendix A.5. |