In recent years, machine learning (ML) and deep learning (DL) have shown remarkable
improvement in computer vision, natural language processing, stock prediction, forecasting …

The rapid uptake of mobile devices and the rising popularity of mobile applications and
services pose unprecedented demands on mobile and wireless networking infrastructure …

This paper presents the machine learning architecture of the Snips Voice Platform, a
software solution to perform Spoken Language Understanding on microprocessors typical of …

Abstract Time Delay Neural Networks (TDNNs), also known as onedimensional
Convolutional Neural Networks (1-d CNNs), are an efficient and well-performing neural …

Neural machine translation (NMT) offers a novel alternative formulation of translation that is
potentially simpler than statistical approaches. However to reach competitive performance …

In recent years, the fields of natural language processing (NLP) and information retrieval (IR)
have made tremendous progress thanks to deep learning models like Recurrent Neural …

Neural Machine Translation (NMT), like many other deep learning domains, typically suffers
from over-parameterization, resulting in large storage sizes. This paper examines three …

In this paper, we summarize recent progresses made in deep learning based acoustic
models and the motivation and insights behind the surveyed techniques. We first discuss …

We describe a large vocabulary speech recognition system that is accurate, has low latency,
and yet has a small enough memory and computational footprint to run faster than real-time …

Recent years have witnessed an exponential increase in the use of mobile and embedded
devices. With the great success of deep learning in many fields, there is an emerging trend …