The rise of powerful large language models (LLMs) brings about tremendous opportunities
for innovation but also looming risks for individuals and society at large. We have reached a …

Learning to reject is a special kind of self-awareness (the ability to know what you do not
know), which is an essential factor for humans to become smarter. Although machine …

The ability to detect out-of-distribution (OOD) samples is vital to secure the reliability of deep
neural networks in real-world applications. Considering the nature of OOD samples …

Surrogate models for partial differential equations are widely used in the design of
metamaterials to rapidly evaluate the behavior of composable components. However, the …

The ability to estimate the uncertainty of predictions made by a neural network is essential
when applying neural networks to tasks such as medical diagnosis and autonomous …

It is known that neural networks have the problem of being over-confident when directly
using the output label distribution to generate uncertainty measures. Existing methods …

Accurately estimating uncertainties in neural network predictions is of great importance in
building trusted DNNs-based models, and there is an increasing interest in providing …

In this paper, we describe an open source Python toolkit named Uncertainty Quantification
360 (UQ360) for the uncertainty quantification of AI models. The goal of this toolkit is twofold …

Total suspended particulates (TSP), as a key pollutant, is a serious threat for air quality,
climate, ecosystems and human health. Therefore, measurements, prediction and …

Abstract Deep Neural Networks (DNNs) have achieved state-of-the-art accuracy
performance in many tasks. However, recent works have pointed out that the outputs …