Can AlphaX framework be adapted to improve sample efficiency in neural architecture search by incorporating un

Abstract

Abstract: Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a network learns a function with very high variance such as to perfectly model the training data. Unfortunately, many application domains do not have access to big data, such as medical image analysis. This survey focuses on Data Augmentation, a data-space solution to the problem of limited data. Data Augmentation encompasses a suite of techniques that enhance the size and quality

Research Question

Can AlphaX framework be adapted to improve sample efficiency in neural architecture search by incorporating uncertainty quantification through Bayesian neural networks with fewer Monte Carlo samples?

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Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

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Provenance