Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
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 | Input Tokens: 16,505 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,107 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |