Deep learning has revolutionized the fields of computer vision, natural language
understanding, speech recognition, information retrieval, and more. However, with the …

The Industry 4.0 literature has exponentially grown in the past decade. We aim to
understand how this literature has evolved and propose future research opportunities. We …

Image segmentation is usually addressed by training a model for a fixed set of object
classes. Incorporating additional classes or more complex queries later is expensive as it …

Distributed word representations, or word vectors, have recently been applied to many tasks
in natural language processing, leading to state-of-the-art performance. A key ingredient to …

The Arabic language is a morphologically rich language with relatively few resources and a
less explored syntax compared to English. Given these limitations, Arabic Natural Language …

State-of-the-art semantic segmentation methods require sufficient labeled data to achieve
good results and hardly work on unseen classes without fine-tuning. Few-shot segmentation …

In this paper we study yes/no questions that are naturally occurring---meaning that they are
generated in unprompted and unconstrained settings. We build a reading comprehension …

Adversarial examples highlight model vulnerabilities and are useful for evaluation and
interpretation. We define universal adversarial triggers: input-agnostic sequences of tokens …

Although much effort has recently been devoted to training high-quality sentence
embeddings, we still have a poor understanding of what they are capturing." Downstream" …

Pre-training text representations have led to significant improvements in many areas of
natural language processing. The quality of these models benefits greatly from the size of …