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..
Combining Generative and Discriminative Models for Hybrid Inference
Authors: Victor Garcia Satorras, Zeynep Akata, Max Welling
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our ideas to the Kalman filter, a Gaussian hidden Markov model for time sequences, and show, among other things, that our model can estimate the trajectory of a noisy chaotic Lorenz Attractor much more accurately than either the learned or graphical inference run in isolation. |
| Researcher Affiliation | Collaboration | Victor Garcia Satorras Uv A-Bosch Delta Lab University of Amsterdam Netherlands EMAIL Zeynep Akata Cluster of Excellence ML University of Tübingen Germany EMAIL Max Welling Uv A-Bosch Delta Lab University of Amsterdam Netherlands EMAIL |
| Pseudocode | No | No. The paper describes the model through equations and textual descriptions but does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | 2Available at: https://github.com/vgsatorras/hybrid-inference |
| Open Datasets | Yes | To demonstrate the generalizability of our Hybrid model to real world datasets, we use the Michigan NCLT [6] dataset which is collected by a segway robot moving around the University of Michigan s North Campus. |
| Dataset Splits | Yes | We sample two different motion trajectories from 50 to 100K time steps each, one for validation and the other for training. An additional 10K time steps trajectory is sampled for testing. |
| Hardware Specification | No | No. The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | No. The paper mentions software components like 'Adam optimizer', 'MLPs', and 'GRU', but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We set γ = 0.005 and use the Adam optimizer with a learning rate 10 3. The number of inference iterations used in the Hybrid model, GNN-messages and GM-messages is N=50. fe and fdec are a 2-layers MLPs with Leaky Relu and Relu activations respectively. The number of features in the hidden layers of the GRU, fe and fdec is nf=48. |