Abstract: End-to-end automatic speech recognition (E2E-ASR) can be classified by its decoder architectures, such as connectionist temporal classification (CTC), recurrent neural network transducer ...

In the summer of 2017, a group of Google Brain researchers quietly published a paper that would forever change the trajectory of artificial intelligence. Titled "Attention Is All You Need," this ...

The first sequence-to-sequence (seq2seq) model was introduced by researchers at Google in Dec 2014. The model was described in the “Sequence to Sequence Learning with Neural Networks” paper by Ilya ...

NLP is about the sequence of words and sentences and that's where sequence modeling with RNN emerged. A base foundation model in NLP is to predict the next word of your sentence. The Recurrent Neural ...

Deep learning transformer-based models using longitudinal electronic health records (EHRs) have shown a great success in prediction of clinical diseases or outcomes. Pretraining on a large dataset can ...

The recent breakthroughs of Large Language Models (LLMs) in the context of natural language processing have opened the way to significant advances in protein research. Indeed, the relationships ...

Back in the old days, traditional phrase-based translation systems performed their task by breaking up source sentences into multiple chunks and then translated them phrase-by-phrase. This led to ...

Abstract: In this article, a stochastic recurrent encoder decoder neural network (SREDNN), which considers latent random variables in its recurrent structures, is developed for the first time for the ...

The uncertainty and fluctuation are the major challenges casted by the large penetration of wind power (WP). As one of the most important solutions for tackling these issues, accurate forecasting is ...