Recently, the speech community is seeing a significant trend of moving from deep neural
network based hybrid modeling to end-to-end (E2E) modeling for automatic speech …

In this paper, we propose a remote-sensing scene-classification method based on vision
transformers. These types of networks, which are now recognized as state-of-the-art models …

Transformers have recently dominated the ASR field. Although able to yield good
performance, they involve an autoregressive (AR) decoder to generate tokens one by one …

Non-autoregressive (NAR) generation, which is first proposed in neural machine translation
(NMT) to speed up inference, has attracted much attention in both machine learning and …

Joint entity and relation extraction is an important task in natural language processing, which
aims to extract all relational triples mentioned in a given sentence. In essence, the relational …

In the clinical and healthcare domains, medical images play a critical role. A mature medical
visual question answering system (VQA) can improve diagnosis by answering clinical …

We present a simple and efficient auxiliary loss function for automatic speech recognition
(ASR) based on the connectionist temporal classification (CTC) objective. The proposed …

We present Mask CTC, a novel non-autoregressive end-to-end automatic speech
recognition (ASR) framework, which generates a sequence by refining outputs of the …

Background In the domain of natural language processing, Transformers are recognized as
state‐of‐the‐art models, which opposing to typical convolutional neural networks (CNNs) do …

This paper presents the Imputer, a neural sequence model that generates output sequences
iteratively via imputations. The Imputer is an iterative generation model, requiring only a …