Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …

Recently, the emergence of pre-trained models (PTMs) has brought natural language
processing (NLP) to a new era. In this survey, we provide a comprehensive review of PTMs …

Deep learning methods are achieving ever-increasing performance on many artificial
intelligence tasks. A major limitation of deep models is that they are not amenable to …

We consider the problem of explaining the predictions of graph neural networks (GNNs),
which otherwise are considered as black boxes. Existing methods invariably focus on …

Language models pretrained on text from a wide variety of sources form the foundation of
today's NLP. In light of the success of these broad-coverage models, we investigate whether …

The goal of this work is to build flexible video-language models that can generalize to
various video-to-text tasks from few examples. Existing few-shot video-language learners …

The increasing use of English as a Lingua Franca (ELF) worldwide has brought attention to
tools that can assist English as a Foreign Language (EFL) learners in their journey to …

Aspect-based sentiment analysis aims to determine the sentiment polarity towards a specific
aspect in online reviews. Most recent efforts adopt attention-based neural network models to …

Sentence embeddings are an important component of many natural language processing
(NLP) systems. Like word embeddings, sentence embeddings are typically learned on large …

Despite recent success in using the invariance principle for out-of-distribution (OOD)
generalization on Euclidean data (eg, images), studies on graph data are still limited …