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..
Variational Gaussian Process
Authors: Dustin Tran, Rajesh Ranganath, David Blei
ICLR 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We study the VGP on standard benchmarks for unsupervised learning, applying it to perform inference in deep latent Gaussian models (Rezende et al., 2014) and DRAW (Gregor et al., 2015), a latent attention model. For both models, we report the best results to date. |
| Researcher Affiliation | Academia | Dustin Tran Harvard University EMAIL Rajesh Ranganath Princeton University EMAIL David M. Blei Columbia University EMAIL |
| Pseudocode | Yes | Algorithm 1: Black box inference with a variational Gaussian process |
| Open Source Code | No | The paper mentions using existing tools like Stan and Theano, but it does not state that its own code for the described methodology is open-source or provide a link to it. |
| Open Datasets | Yes | The binarized MNIST data set (Salakhutdinov & Murray, 2008) consists of 28x28 pixel images with binary-valued outcomes. |
| Dataset Splits | No | The paper mentions a split for the Sketch dataset ('We partition it into 18,000 training examples and 2,000 test examples') but does not specify a separate validation split or detail a cross-validation strategy. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'Stan and Theano' for differentiation tools but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | For the learning rate we apply a version of RMSProp (Tieleman & Hinton, 2012), in which we scale the value with a decaying schedule 1/t1/2+ϵ for ϵ > 0. We fix the size of variational data to be 500 across all experiments and set the latent input dimension equal to the number of latent variables. |