Mar 6, 2019 · We design a novel model, referred to as 'Enhanced Deep Recurrent Denoising Auto-Encoder' (EDRDAE), that incorporates a signal amplifier layer, and applies ...
Denoising auto-encoders (DAEs) follow the architecture of AEs, a type of unsupervised deep net learns an efficient data representation [3, 4, 5], mapping an ...
This paper proposes a novel deep-learning-based method that uses a 9-layer convolutional denoising autoencoder (CDAE) to separate the EoR signal.
Mar 6, 2019 · We showcase the performance of EDRDAE using time-series data that describes gravitational waves embedded in very noisy backgrounds. In addition, ...
Denoising of time domain data is a crucial task for many applications such as communication, translation, virtual assistants etc.
This study described how to combine recurrent neural networks with denoising auto-encoders to clean up modeled waveforms embedded in real advanced LIGO noise. .
Nov 27, 2017 · We introduce SMTDAE, a Staired Multi-Timestep Denoising Autoencoder, based on sequence-to-sequence bi-directional Long-Short-Term-Memory recurrent neural ...
Missing: Enhanced | Show results with:Enhanced
Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-encoders. Published 05/01/2019. Publication ICASSP 2019 - 2019 IEEE International ...
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Deep Learning III. Location: Poster Area G, East Landing, First Floor ... Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders.
In this paper, we proposed a new deep recurrent denoising auto- encoder to denoise gravitational wave signals contaminated by an extremely high level of noise ...