scholarly journals End-to-End Risk Budgeting Portfolio Optimization with Neural Networks

2021 ◽  
Author(s):  
Ayse Sinem Uysal ◽  
Xiaoyue Li ◽  
John M. Mulvey
Keyword(s):  
Neural Networks ◽  
Portfolio Optimization ◽  
Risk Budgeting ◽  
End To End

scholarly journals Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems

2021 ◽  
Vol 11 (7) ◽  
pp. 2925
Author(s):  
Edgar Cortés Gallardo Medina ◽  
Victor Miguel Velazquez Espitia ◽  
Daniela Chípuli Silva ◽  
Sebastián Fernández Ruiz de las Cuevas ◽  
Marco Palacios Hirata ◽  
...  
Keyword(s):  
Neural Networks ◽  
Cloud Computing ◽  
Path Planning ◽  
Object Detection ◽  
Short Term Memory ◽  
Smart Cities ◽  
Autonomous Vehicle ◽  
Computational Time ◽  
End To End ◽  
On The Road

Autonomous vehicles are increasingly becoming a necessary trend towards building the smart cities of the future. Numerous proposals have been presented in recent years to tackle particular aspects of the working pipeline towards creating a functional end-to-end system, such as object detection, tracking, path planning, sentiment or intent detection, amongst others. Nevertheless, few efforts have been made to systematically compile all of these systems into a single proposal that also considers the real challenges these systems will have on the road, such as real-time computation, hardware capabilities, etc. This paper reviews the latest techniques towards creating our own end-to-end autonomous vehicle system, considering the state-of-the-art methods on object detection, and the possible incorporation of distributed systems and parallelization to deploy these methods. Our findings show that while techniques such as convolutional neural networks, recurrent neural networks, and long short-term memory can effectively handle the initial detection and path planning tasks, more efforts are required to implement cloud computing to reduce the computational time that these methods demand. Additionally, we have mapped different strategies to handle the parallelization task, both within and between the networks.

An Automated End-To-End Pipeline for Fine-Grained Video Annotation using Deep Neural Networks

2016 ◽  
Author(s):  
Baptist Vandersmissen ◽  
Lucas Sterckx ◽  
Thomas Demeester ◽  
Azarakhsh Jalalvand ◽  
Wesley De Neve ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Video Annotation ◽  
Fine Grained ◽  
End To End

Tackling Contradiction Detection in German Using Machine Translation and End-to-End Recurrent Neural Networks

2021 ◽  
Author(s):  
Maren Pielka ◽  
Rafet Sifa ◽  
Lars Patrick Hillebrand ◽  
David Biesner ◽  
Rajkumar Ramamurthy ◽  
...  
Keyword(s):  
Neural Networks ◽  
Machine Translation ◽  
Recurrent Neural Networks ◽  
End To End

IMAGEimate - An End-to-End Pipeline to Create Realistic Animatable 3D Avatars from a Single Image Using Neural Networks

2021 ◽  
Author(s):  
Suriya Dakshina Murthy ◽  
Tobias Höllerer ◽  
Misha Sra
Keyword(s):  
Neural Networks ◽  
Single Image ◽  
End To End

An End-to-End Sleep Staging Simulator Based on Mixed Deep Neural Networks

2021 ◽  
Author(s):  
Zheng Chen ◽  
Ziwei Yang ◽  
Dong Wang ◽  
Ming Huang ◽  
Naoaki Ono ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Sleep Staging ◽  
End To End
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