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Found 4 papers, 1 papers with code
How Flawed is ECE? An Analysis via Logit Smoothing
Informally, a model is calibrated if its predictions are correct with a probability that matches the confidence of the prediction.
Renormalizing Diffusion Models
We explain how to use diffusion models to learn inverse renormalization group flows of statistical and quantum field theories.
Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering
In this work, we propose a novel neural scene representation, Light Field Networks or LFNs, which represent both geometry and appearance of the underlying 3D scene in a 360-degree, four-dimensional light field parameterized via a neural implicit representation.
Stochastic natural gradient descent draws posterior samples in function space
Recent work has argued that stochastic gradient descent can approximate the Bayesian uncertainty in model parameters near local minima.
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