With the rapid development of the Internet, the massive amount of web textual data has
grown exponentially, which has brought considerable challenges to downstream tasks, such …

Neural sequence-to-sequence models have provided a viable new approach for abstractive
text summarization (meaning they are not restricted to simply selecting and rearranging …

In this work, we model abstractive text summarization using Attentional Encoder-Decoder
Recurrent Neural Networks, and show that they achieve state-of-the-art performance on two …

Out of all existing frameworks for surgical workflow analysis in endoscopic videos, action
triplet recognition stands out as the only one aiming to provide truly fine-grained and …

In the past few years, neural abstractive text summarization with sequence-to-sequence
(seq2seq) models have gained a lot of popularity. Many interesting techniques have been …

Abstractive summarization aims to generate a shorter version of the document covering all
the salient points in a compact and coherent fashion. On the other hand, query-based …

Encoder-decoder models have become an effective approach for sequence learning tasks
like machine translation, image captioning and speech recognition, but have yet to show …

Environmental sound classification (ESC) is a challenging problem due to the complexity of
sounds. The classification performance is heavily dependent on the effectiveness of …

The gear fault signal under different working conditions is non-linear and non-stationary,
which makes it difficult to distinguish faulty signals from normal signals. Currently, gear fault …

Audio tagging is the task of predicting the presence or absence of sound classes within an
audio clip. Previous work in audio tagging focused on relatively small datasets limited to …