Despite the availability and ease of collecting a large amount of free, unlabeled data, the
expensive and time-consuming labeling process is still an obstacle to labeling a sufficient …

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 …

Bayesian approaches developed to solve the optimal design of sequential experiments are
mathematically elegant but computationally challenging. Recently, techniques using …

Meta-learning has demonstrated promising results in few-shot classification (FSC) by
learning to solve new problems using prior knowledge. Bayesian methods are effective at …

Understanding the phase stability of a chemical system constitutes the foundation of
materials science. Knowledge of the equilibrium state of a system under arbitrary …

The effectiveness of active learning largely depends on the sampling efficiency of the
acquisition function. Expected Loss Reduction (ELR) focuses on a Bayesian estimate of the …

This paper introduces a computationally efficient Query-by-Committee (QBC) algorithm
specifically designed for deep active learning. This approach leverages the concept of …

At its core, this thesis aims to enhance the practicality of deep learning by improving the
label and training efficiency of deep learning models. To this end, we investigate data subset …

Active learning is a machine learning paradigm that aims to improve the performance of a
model by strategically selecting and querying unlabeled data. One effective selection …

Significant acceleration of the future discovery of novel functional materials requires a
fundamental shift from the current materials discovery practice, which is heavily dependent …