scholarly journals A model‐driven robust deep learning wireless transceiver

2021 ◽  
Author(s):  
Sirui Duan ◽  
Jingyi Xiang ◽  
Xiang Yu
Keyword(s):  
Deep Learning ◽  
Model Driven ◽  
Wireless Transceiver

scholarly journals Fast inverse lithography based on dual-channel model-driven deep learning

2020 ◽  
Vol 28 (14) ◽  
pp. 20404 ◽  
Author(s):  
Xu Ma ◽  
Xianqiang Zheng ◽  
Gonzalo R. Arce
Keyword(s):  
Deep Learning ◽  
Channel Model ◽  
Model Driven ◽  
Dual Channel

scholarly journals Model-Driven Deep Learning for Massive Multiuser MIMO Constant Envelope Precoding

2020 ◽  
Vol 9 (11) ◽  
pp. 1835-1839
Author(s):  
Yunfeng He ◽  
Hengtao He ◽  
Chao-Kai Wen ◽  
Shi Jin
Keyword(s):  
Deep Learning ◽  
Multiuser Mimo ◽  
Model Driven ◽  
Constant Envelope

Model-driven Deep Learning Method for Pancreatic Cancer Segmentation Based on Spiral-transformation

2021 ◽  
pp. 1-1
Author(s):  
Xiahan Chen ◽  
Zihao Chen ◽  
Jun Li ◽  
Yu-Dong Zhang ◽  
Xiaozhu Lin ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Deep Learning ◽  
Learning Method ◽  
Model Driven

scholarly journals The limits and potentials of deep learning for robotics

2018 ◽  
Vol 37 (4-5) ◽  
pp. 405-420 ◽  
Author(s):  
Niko Sünderhauf ◽  
Oliver Brock ◽  
Walter Scheirer ◽  
Raia Hadsell ◽  
Dieter Fox ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Communities ◽  
Data Driven ◽  
Important Research ◽  
Research Directions ◽  
Model Driven ◽  
Research Questions ◽  
Robotic Learning ◽  
Specific Learning

The application of deep learning in robotics leads to very specific problems and research questions that are typically not addressed by the computer vision and machine learning communities. In this paper we discuss a number of robotics-specific learning, reasoning, and embodiment challenges for deep learning. We explain the need for better evaluation metrics, highlight the importance and unique challenges for deep robotic learning in simulation, and explore the spectrum between purely data-driven and model-driven approaches. We hope this paper provides a motivating overview of important research directions to overcome the current limitations, and helps to fulfill the promising potentials of deep learning in robotics.

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