scholarly journals BeamLearning: An end-to-end deep learning approach for the angular localization of sound sources using raw multichannel acoustic pressure data

2021 ◽  
Vol 149 (6) ◽  
pp. 4248-4263
Author(s):  
Hadrien Pujol ◽  
Éric Bavu ◽  
Alexandre Garcia
Keyword(s):  
Deep Learning ◽  
Acoustic Pressure ◽  
Learning Approach ◽  
Pressure Data ◽  
Sound Sources ◽  
End To End

DeepSpectra: An end-to-end deep learning approach for quantitative spectral analysis

2019 ◽  
Vol 1058 ◽  
pp. 48-57 ◽  
Author(s):  
Xiaolei Zhang ◽  
Tao Lin ◽  
Jinfan Xu ◽  
Xuan Luo ◽  
Yibin Ying
Keyword(s):  
Spectral Analysis ◽  
Deep Learning ◽  
Learning Approach ◽  
Quantitative Spectral Analysis ◽  
End To End

scholarly journals DeepVar: An End-to-End Deep Learning Approach for Genomic Variant Recognition in Biomedical Literature

2020 ◽  
Vol 34 (01) ◽  
pp. 598-605
Author(s):  
Chaoran Cheng ◽  
Fei Tan ◽  
Zhi Wei
Keyword(s):  
Deep Learning ◽  
Large Data ◽  
Biomedical Literature ◽  
Entity Recognition ◽  
Learning Approach ◽  
Learning Approaches ◽  
Genomic Variants ◽  
Low Resource ◽  
End To End ◽  
Genomic Variant

We consider the problem of Named Entity Recognition (NER) on biomedical scientific literature, and more specifically the genomic variants recognition in this work. Significant success has been achieved for NER on canonical tasks in recent years where large data sets are generally available. However, it remains a challenging problem on many domain-specific areas, especially the domains where only small gold annotations can be obtained. In addition, genomic variant entities exhibit diverse linguistic heterogeneity, differing much from those that have been characterized in existing canonical NER tasks. The state-of-the-art machine learning approaches heavily rely on arduous feature engineering to characterize those unique patterns. In this work, we present the first successful end-to-end deep learning approach to bridge the gap between generic NER algorithms and low-resource applications through genomic variants recognition. Our proposed model can result in promising performance without any hand-crafted features or post-processing rules. Our extensive experiments and results may shed light on other similar low-resource NER applications.

scholarly journals PestNet: An End-to-End Deep Learning Approach for Large-Scale Multi-Class Pest Detection and Classification

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 45301-45312 ◽  
Author(s):  
Liu Liu ◽  
Rujing Wang ◽  
Chengjun Xie ◽  
Po Yang ◽  
Fangyuan Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Learning Approach ◽  
Pest Detection ◽  
End To End
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