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 [1].
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes
Authors: Tim G. J. Rudner, Oscar Key, Yarin Gal, Tom Rainforth
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Specifically, we show both theoretically and via an extensive empirical evaluation that the SNR of the gradient estimates for the latent variable s variational parameters decreases as the number of importance samples increases. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Oxford, Oxford, United Kingdon 2Computer Science Department, University College London, London, United Kingdom. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/ timrudner/snr issues in deep gps. |
| Open Datasets | Yes | Dua, D. and Graff, C. UCI machine learning repository, 2017. URL http://archive.ics.uci.edu/ml. |
| Dataset Splits | No | The paper states '20 random train-test splits' but does not explicitly mention a separate validation set or its split percentage. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions open-source code availability but does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For these experiments, we consider a two-layer DGP with the hyperparameters that directly affect the SNR the number of importance samples K and the minibatch size set to 50 and 64, respectively. |