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Unified Probabilistic Neural Architecture and Weight Ensembling Improves Model Robustness by Premchandar, Sumegha; Madireddy, Sandeep; Jantre, Sanket; Balaprakash, Prasanna is a scholarly article available to read on EtoBox.

What is Unified Probabilistic Neural Architecture and Weight Ensembling Improves Model Robustness about?

Robust machine learning models with accurately calibrated uncertainties are crucial for safety-critical applications. Probabilistic machine learning and especially the Bayesian formalism provide a systematic framework to incorporate robustness through the distributional estimates and reason about uncertainty. Recent works have shown that approximate inference approaches that take the weight space uncertainty of neural networks to generate ensemble prediction are the state-of-the-art. However, architecture choices have mostly been ad hoc, which essentially ignores the epistemic uncertainty from the architecture space. To this end, we propose a Unified probabilistic architecture and weight ensembling Neural Architecture Search (UraeNAS) that leverages advances in probabilistic neural architecture search and approximate Bayesian inference to generate ensembles form the joint distribution of neural network architectures and weights. The proposed approach showed a significant improvement both with in-distribution (0.86% in accuracy, 42% in ECE) CIFAR-10 and out-of-distribution (2.43% in accuracy, 30% in ECE) CIFAR-10-C compared to the baseline deterministic approach.

Author

Premchandar, Sumegha; Madireddy, Sandeep; Jantre, Sanket; Balaprakash, Prasanna

Published

2022

Language

EN