In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …

Active learning (AL) attempts to maximize a model's performance gain while annotating the
fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount …

Large datasets have become commonplace in NLP research. However, the increased
emphasis on data quantity has made it challenging to assess the quality of data. We …

In this paper, we present a comprehensive review of the imbalance problems in object
detection. To analyze the problems in a systematic manner, we introduce a problem-based …

Large neural models have demonstrated human-level performance on language and vision
benchmarks, while their performance degrades considerably on adversarial or out-of …

Abstract Neural Architecture Search (NAS) achieves significant progress in many computer
vision tasks. While many methods are proposed to improve the efficiency of NAS, the search …

In the current development and deployment of many artificial intelligence (AI) systems in
healthcare, algorithm fairness is a challenging problem in delivering equitable care. Recent …

Self-supervised learning has recently begun to rival supervised learning on computer vision
tasks. Many of the recent approaches have been based on contrastive instance …

Abstract Convolutional Neural Networks (CNNs) have proven to be state-of-the-art models
for supervised computer vision tasks, such as image classification. However, large labeled …

The problem of learning an image classifier that allows detection of out-of-distribution (OOD)
examples, with the help of auxiliary background datasets, is studied. While training with …