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Researcher profile

Tim G. J. Rudner

2 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes

    2021 · International Conference on Machine Learning

    We show that the gradient estimates used in training Deep Gaussian Processes (DGPs) with importance-weighted variational inference are susceptible to signal-to-noise ratio (SNR) issues. Specifically, we show both theoretically and via an extensive empirical evaluation …

  2. Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks

    2022 · arXiv (Cornell University)

    Bayesian deep learning seeks to equip deep neural networks with the ability to precisely quantify their predictive uncertainty, and has promised to make deep learning more reliable for safety-critical real-world applications. Yet, existing Bayesian deep …