GAIA Gradient-Based Attribution for OOD Detection

Deep neural networks (DNNs) have shown incredible accuracy across numerous applications. However, their inability to handle out-of-distribution (OOD) samples can lead to unpredictable and potentially unsafe behavior. This post explores the recent paper on the Gradient Abnormality Inspection and Aggregation (GAIA)(Chen et al., 2023) framework, which introduces an innovative approach to enhance OOD detection.

Gradient-aware...

Survey on Uncertainty Estimation in Deep Learning

A distinction between aleatoric and epistemic uncertainties is proposed in the domain of medical decision-making (Senge et al., 2014). Their paper explained that aleatoric and epistemic uncertainties are not distinguished in Bayesian inference. Moreover, the expectation over the model with respect to the posterior is used to get our prediction leading to an averaged epistemic...

Empowering the Next Generation My Journey with MISE

In the ever-evolving landscape of artificial intelligence, there’s a pressing need to cultivate diverse talent and foster inclusivity in STEM fields. One initiative that has personally enriched my journey and allowed me to contribute meaningfully to this cause is MISE (Machine Learning in Science and Engineering), a transformative program dedicated to equipping high school...