Convolutional neural network (CNN)-based deep learning (DL) is a powerful, recently
developed image classification approach. With origins in the computer vision and image …

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

Pretrained general-purpose language models can achieve state-of-the-art accuracies in
various natural language processing domains by adapting to downstream tasks via zero …

We show that a GPT-3 model can learn to express uncertainty about its own answers in
natural language--without use of model logits. When given a question, the model generates …

Deep neural networks may perform poorly when training datasets are heavily class-
imbalanced. Recently, two-stage methods decouple representation learning and classifier …

In a real-world scenario, human actions are typically out of the distribution from training data,
which requires a model to both recognize the known actions and reject the unknown …

As a main field of artificial intelligence, natural language processing (NLP) has achieved
remarkable success via deep neural networks. Plenty of NLP tasks have been addressed in …

Capturing accurate uncertainty quantification of the prediction from deep neural networks is
important in many real-world decision-making applications. A reliable predictor is expected …

Therapeutic antibodies make up a rapidly growing segment of the biologics market.
However, rational design of antibodies is hindered by reliance on experimental methods for …

Imbalanced class distribution is an inherent problem in many real-world classification tasks
where the minority class is the class of interest. Many conventional statistical and machine …