Bayesian neural networks trained on synthetic EHT observations of Sgr A* and M87* recover spin and magnetic state well in cross-code tests, but give overconfident wrong estimates for temperature ratio and inclination when the training grid is sparse.
The What-If Tool: Interactive Probing of Machine Learning Models
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abstract
A key challenge in developing and deploying Machine Learning (ML) systems is understanding their performance across a wide range of inputs. To address this challenge, we created the What-If Tool, an open-source application that allows practitioners to probe, visualize, and analyze ML systems, with minimal coding. The What-If Tool lets practitioners test performance in hypothetical situations, analyze the importance of different data features, and visualize model behavior across multiple models and subsets of input data. It also lets practitioners measure systems according to multiple ML fairness metrics. We describe the design of the tool, and report on real-life usage at different organizations.
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2025 1verdicts
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Deep learning inference with the Event Horizon Telescope II. The Zingularity framework for Bayesian artificial neural networks
Bayesian neural networks trained on synthetic EHT observations of Sgr A* and M87* recover spin and magnetic state well in cross-code tests, but give overconfident wrong estimates for temperature ratio and inclination when the training grid is sparse.