The field of natural language processing has seen impressive progress in recent years, with
neural network models replacing many of the traditional systems. A plethora of new models …

Interactive machine learning (IML) is an iterative learning process that tightly couples a
human with a machine learner, which is widely used by researchers and practitioners to …

Recent progress in natural language processing has been driven by advances in both
model architecture and model pretraining. Transformer architectures have facilitated …

A key challenge in developing and deploying Machine Learning (ML) systems is
understanding their performance across a wide range of inputs. To address this challenge …

Machine-learning models have demonstrated great success in learning complex patterns
that enable them to make predictions about unobserved data. In addition to using models for …

Although decision‐making algorithms are not new to medicine, the availability of vast stores
of medical data, gains in computing power, and breakthroughs in machine learning are …

Machine-learning models have demonstrated great success in learning complex patterns
that enable them to make predictions about unobserved data. In addition to using models for …

We propose a framework for interactive and explainable machine learning that enables
users to (1) understand machine learning models;(2) diagnose model limitations using …

Large language models can produce powerful contextual representations that lead to
improvements across many NLP tasks. Since these models are typically guided by a …

Leveraging both temporal and spatial correlations to predict wind speed remains one of the
most challenging and less studied areas of wind speed prediction. In this paper, the problem …