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Researcher profile

Graham W. Taylor

5 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks

    2017 · arXiv (Cornell University)

    In this work, we propose CLass-Enhanced Attentive Response (CLEAR): an approach to visualize and understand the decisions made by deep neural networks (DNNs) given a specific input. CLEAR facilitates the visualization of attentive regions and …

  2. Federated Learning and Differential Privacy for Medical Image Analysis

    2021 · Research Square

    <title>Abstract</title> The artificial intelligence revolution has been spurred forward by the availability of large-scale datasets. In contrast, the paucity of large-scale medical datasets hinders the application of machine learning in healthcare. The lack of publicly …

  3. Bounding generalization error with input compression: An empirical study with infinite-width networks

    2022 · arXiv (Cornell University)

    Estimating the Generalization Error (GE) of Deep Neural Networks (DNNs) is an important task that often relies on availability of held-out data. The ability to better predict GE based on a single training set may …

  4. Empirically Validating Conformal Prediction on Modern Vision Architectures Under Distribution Shift and Long-tailed Data

    2023 · arXiv (Cornell University)

    Conformal prediction has emerged as a rigorous means of providing deep learning models with reliable uncertainty estimates and safety guarantees. Yet, its performance is known to degrade under distribution shift and long-tailed class distributions, which …

  5. Adapting Prediction Sets to Distribution Shifts Without Labels

    2024 · arXiv (Cornell University)

    Recently there has been a surge of interest to deploy confidence set predictions rather than point predictions in machine learning. Unfortunately, the effectiveness of such prediction sets is frequently impaired by distribution shifts in practice, …