Variational Gaussian Process
Authors: Dustin Tran, Rajesh Ranganath, David Blei
ICLR 2016 | Conference PDF | Archive PDF | Plain Text | 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 dtran@g.harvard.edu Rajesh Ranganath Princeton University rajeshr@cs.princeton.edu David M. Blei Columbia University david.blei@columbia.edu |
| 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. |