Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

In this work, we propose learnable Bernoulli dropout (LBD), a new model-agnostic dropout
scheme that considers the dropout rates as parameters jointly optimized with other model …

Continual Learning (CL) is the problem of sequentially learning a set of tasks and preserving
all the knowledge acquired. Many existing methods assume that the data stream is explicitly …

Cardiovascular disorders cause nearly one in three deaths in the United States. Short-and
long-term care for these disorders is often determined in short-term settings. However, these …

Activity recognition in wearable computing faces two key challenges: i) activity
characteristics may be context-dependent and change under different contexts or situations; …

In many machine learning tasks, input features with varying degrees of predictive capability
are acquired at varying costs. In order to optimize the performance-cost trade-off, one would …

The utility of machine learning for enhancing human well-being and health has risen to the
core discussion in both research and real-world application in today's technological front …

Machine learning techniques are widely used to build models for applications in healthcare.
These models typically predict likelihood of a particular patient outcome in a given setting …

In many machine learning tasks, input features with varying degrees of predictive capability
are usually acquired at some cost. For example, in human activity recognition (HAR) and …