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..
Multi-Source Neural Variational Inference
Authors: Richard Kurle, Stephan Günnemann, Patrick van der Smagt4114-4121
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We visualise learned beliefs on a toy dataset and evaluate our methods for learning shared representations and structured output prediction, showing trade-offs of learning separate encoders for each information source. Furthermore, we demonstrate how conflict detection and redundancy can increase robustness of inference in a multi-source setting. |
| Researcher Affiliation | Collaboration | Richard Kurle Department of Informatics Technical University of Munich, Data:Lab, Volkswagen Group 80805 Munich, Germany EMAIL Stephan G unnemann Department of Informatics Technical University of Munich EMAIL Patrick van der Smagt Data:Lab, Volkswagen Group 80805 Munich, Germany |
| Pseudocode | No | The paper does not contain pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about open-source code availability or links to code repositories. |
| Open Datasets | Yes | We created 3 variants of MNIST (Lecun et al. 1998); Caltech-UCSD Birds 200 (Welinder et al. 2010) |
| Dataset Splits | No | The paper specifies training and test splits for the Caltech-UCSD Birds 200 dataset, but does not explicitly mention a separate validation split or its size for any dataset used. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies. |
| Experiment Setup | No | Model and algorithm hyperparameters are summarised in the supplementary material of our technical report (Kurle, G unnemann, and Smagt 2018). |