Translation Between Waves, wave2wave

07/20/2020
by   Tsuyoshi Okita, et al.
0

The understanding of sensor data has been greatly improved by advanced deep learning methods with big data. However, available sensor data in the real world are still limited, which is called the opportunistic sensor problem. This paper proposes a new variant of neural machine translation seq2seq to deal with continuous signal waves by introducing the window-based (inverse-) representation to adaptively represent partial shapes of waves and the iterative back-translation model for high-dimensional data. Experimental results are shown for two real-life data: earthquake and activity translation. The performance improvements of one-dimensional data was about 46 and that of high-dimensional data was about 1625 the original seq2seq.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/17/2020

Learning a Deep Part-based Representation by Preserving Data Distribution

Unsupervised dimensionality reduction is one of the commonly used techni...
research
04/25/2023

VeML: An End-to-End Machine Learning Lifecycle for Large-scale and High-dimensional Data

An end-to-end machine learning (ML) lifecycle consists of many iterative...
research
04/30/2022

Testing Overidentifying Restrictions with High-Dimensional Data and Heteroskedasticity

This paper proposes a new test of overidentifying restrictions (called t...
research
04/19/2023

Graph Neural Network-Based Anomaly Detection for River Network Systems

Water is the lifeblood of river networks, and its quality plays a crucia...
research
02/28/2020

Robust Unsupervised Neural Machine Translation with Adversarial Training

Unsupervised neural machine translation (UNMT) has recently attracted gr...
research
08/01/2018

Low-Latency Neural Speech Translation

Through the development of neural machine translation, the quality of ma...

Please sign up or login with your details

Forgot password? Click here to reset