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..
Expectation Propagation with Stochastic Kinetic Model in Complex Interaction Systems
Authors: Le Fang, Fan Yang, Wen Dong, Tong Guan, Chunming Qiao
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate and benchmark the performance of our proposed algorithms (Algorithm 1) against mainstream state-of-the-art approaches. We have the flexibility to specify species, states, and events with different granularities in SKM, at either macroscopic or microscopic level. |
| Researcher Affiliation | Academia | Le Fang, Fan Yang, Wen Dong, Tong Guan, and Chunming Qiao Department of Computer Science and Engineering University at Buffalo EMAIL |
| Pseudocode | Yes | Algorithm 1 Make inference of a stochastic kinetic model with expectation propagation. Input: Discrete time SKM model (Eqs. (1),(2),(3)); Observation probabilities P(yt|xt) and initial values of αt, γt, λt for all populations m and time t. Expectation Propagation fixed point: Alternate between forward and backward iterations until convergence. |
| Open Source Code | No | Source code and a general function interface for other domains at both levels are here online |
| Open Datasets | No | We implement and benchmark algorithms on two representative datasets. In the Synth Town dataset, we synthesize a mini road network (Fig. 1(a)). Virtual residents go to work in the morning and back home in the evening. We synthesize their itineraries from MATSIM, a common Multi-agent transportation simulator[2]. In the Berlin dataset, we have a larger real world road network with 1,539 locations derived from Open Street Map and 9,178 people s itineraries synthesized from MATSIM. The paper mentions synthesizing data from MATSIM but does not provide a direct link or concrete access to the specific generated datasets used for the experiments. |
| Dataset Splits | No | The paper mentions training phases for neural networks but does not explicitly provide specific train/validation/test dataset splits (percentages, counts, or explicit standard split references) for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using MATSIM for data synthesis but does not provide specific version numbers for MATSIM or any other key software dependencies required to replicate the experiment. |
| Experiment Setup | No | The paper mentions tuning meta-parameters and empirically selecting the number of particles but does not provide specific hyperparameter values (e.g., learning rate, batch size, epochs, optimizer settings) or other detailed training configurations for reproducibility. |