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Model-Based Deep Learning: On the Intersection of Deep Learning and Optimization [article]

Nir Shlezinger, Yonina C. Eldar, Stephen P. Boyd
2022 arXiv   pre-print
Model-based optimization and data-centric deep learning are often considered to be distinct disciplines.  ...  The gains of combining model-based optimization and deep learning are demonstrated using experimental results in various applications, ranging from biomedical imaging to digital communications.  ...  This form of model-based deep learning leverages data to optimize the optimizer.  ... 
arXiv:2205.02640v2 fatcat:yclu5hqsx5bv7k2fg4gnfhyrma

Model-Based Deep Learning [article]

Nir Shlezinger, Jay Whang, Yonina C. Eldar, Alexandros G. Dimakis
2021 arXiv   pre-print
Our aim is to facilitate the design and study of future systems on the intersection of signal processing and machine learning that incorporate the advantages of both domains.  ...  In this article we survey the leading approaches for studying and designing model-based deep learning systems.  ...  Finally, while our description of DNN-aided regularized optimization relies on model-based iterative optimizers which utilize a deep learning module, one can also incorporate deep learning into the optimization  ... 
arXiv:2012.08405v2 fatcat:4ilqi3vv4rar5gsveqzo4loqpy

Modeling Adaptive Platoon and Reservation Based Autonomous Intersection Control: A Deep Reinforcement Learning Approach [article]

Duowei Li
2022 arXiv   pre-print
' passing priority; and the second level applies a deep Q-network algorithm to identify the optimal platoon size based on the real-time traffic condition of an intersection.  ...  To this end, this study proposes an adaptive platoon based autonomous intersection control model powered by deep reinforcement learning (DRL) technique.  ...  ACKNOWLEDGMENT The first author would like to acknowledge the State Scholarship Fund provided by the China Scholarship Council that supports her studies in Nanyang Technological University Singapore.  ... 
arXiv:2206.12419v1 fatcat:hp3o3oqlhfg6fimerecxfn7cxi

Cleanup Sketched Drawings: Deep Learning-Based Model

Amal Ahmed Hasan Mohammed, Jiazhou Chen, Fahd Abd Algalil
2022 Applied Bionics and Biomechanics  
This paper demonstrates the efficiency and effectiveness of using deep learning in cleaning and improving the roughly drawn image in an automatic way.  ...  This research paper proposes using a fully convolutional network (FCNN) model to simplify rough raster drawings using deep learning.  ...  Acknowledgments This paper is supported by National Natural Science Foundation of China (62172367) and Natural Science Foundation of Zhejiang Province (LGF22F020022).  ... 
doi:10.1155/2022/2238077 pmid:35578715 pmcid:PMC9107365 fatcat:5wjndf4nbfhfva3wrjrroku6vy

Neural-Attention-Based Deep Learning Architectures for Modeling Traffic Dynamics on Lane Graphs [article]

Matthew A. Wright, Simon F. G. Ehlers, Roberto Horowitz
2019 arXiv   pre-print
topological complexity, and motivate learning dynamics models from many road network topologies.  ...  We consider problems of estimating macroscopic quantities (e.g., the queue at an intersection) at a lane level.  ...  We thank Simon Sohrt of the Institute of Transport and Automation Technology, Leibniz Universität Hannover, for his reading and feedback.  ... 
arXiv:1904.08831v2 fatcat:zi5pb5v5gngxjpnhkkaqi2hxhm

Integrating Deep Reinforcement Learning with Model-based Path Planners for Automated Driving [article]

Ekim Yurtsever, Linda Capito, Keith Redmill, Umit Ozguner
2020 arXiv   pre-print
On the other hand, the more recent, end-to-end Deep Reinforcement Learning (DRL) based model-free ADSs have shown promising results.  ...  However, pure learning-based approaches lack the hard-coded safety measures of model-based controllers.  ...  The proposed system is a hybrid of a model-based planner and a modelfree DRL agent.  ... 
arXiv:2002.00434v2 fatcat:flwuvsgxfbg7jpl5piekkcyevq

Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series [article]

Félix Quinton, Loic Landrieu
2021 arXiv   pre-print
In this paper, we take advantage of the increasing quantity of annotated satellite data to propose the first deep learning approach modeling simultaneously the inter- and intra-annual agricultural dynamics  ...  Along with simple training adjustments, our model provides an improvement of over 6.3 mIoU points over the current state-of-the-art of crop classification.  ...  In conjunction with growing data availability, the adoption of deep learning-based methods has allowed for a large increase in performance for parcel-based crop classification.  ... 
arXiv:2110.08187v2 fatcat:imedafp3zfdjtc7t47zwilh564

Research on Management Model Based on Deep Learning

Yuting Zhao, M. Irfan Uddin
2021 Complexity  
In this study, the focus was on the development of management models and future prediction for the cost and risk by using an improved deep learning (DL) algorithm.  ...  The proposed method is based on dynamic hidden layers with backpropagation and feedforward. Absolute mean relative error (AMRE) and variance (R2) are used for evaluation in term of accuracy.  ...  Acknowledgments e author extends her appreciation to Heilongjiang Province Philosophy and Social Science Fund (19JYE255) for funding and supporting this work.  ... 
doi:10.1155/2021/9997662 fatcat:vanyr55uobd2jj7ggznm3twjw4

Deep-Pneumonia Framework Using Deep Learning Models Based on Chest X-Ray Images

Nada M. Elshennawy, Dina M. Ibrahim
2020 Diagnostics  
Proposing efficient and powerful deep learning models for detecting and classifying pneumonia is the main purpose of this work.  ...  Pneumonia is a contagious disease that causes ulcers of the lungs, and is one of the main reasons for death among children and the elderly in the world.  ...  The experiment performance of our proposed deep learning framework was assessed based on accuracy, precision, F1-score, recall, and AUC, and our model showed values of 99.22%, 99.43%, 99.44%, 99.44%, and  ... 
doi:10.3390/diagnostics10090649 pmid:32872384 pmcid:PMC7554804 fatcat:k5cuvjw3fja63c4ke7owbslkrm

Simplifying Deep-Learning-Based Model for Code Search [article]

Chao Liu, Xin Xia, David Lo, Zhiwei Liu, Ahmed E. Hassan, Shanping Li
2020 arXiv   pre-print
We also observed that: fusing the advantages of IR-based and deep-learning-based models is promising because they compensate with each other by nature; improving the quality of method naming helps code  ...  But they fail to connect the semantic gap between query and code. To conquer this challenge, Gu et al. proposed a deep-learning-based model named DeepCS.  ...  Pros and Cons of Deep-learning-based Model and IR-Based Model The deep-learning-based model has three major advantages over the IR-based model. One is language processing ability.  ... 
arXiv:2005.14373v1 fatcat:vgzlztxrzvf5tbckuv2axkft74

Deep Learning Model with GA based Feature Selection and Context Integration [article]

Ranju Mandal, Basim Azam, Brijesh Verma, Mengjie Zhang
2022 arXiv   pre-print
The novelty of the proposed model is that One-vs-All binary class-based learners are introduced to learn Genetic Algorithm (GA) optimized features in the visual layer, followed by the contextual layer  ...  Since its inception, Many top-performing methods for image segmentation are based on deep CNN models.  ...  The GA-based optimization was investigated to find the optimized set of features to train the classifiers. The proposed deep learning model was evaluated on benchmark datasets.  ... 
arXiv:2204.06189v1 fatcat:qkyclvhyrzed7lt2cimvylj2ou

Generation of Traffic Flows in Multi-Agent Traffic Simulation with Agent Behavior Model based on Deep Reinforcement Learning [article]

Junjie Zhong, Hiromitsu Hattori
2021 arXiv   pre-print
by using deep reinforcement learning based on a combination of regenerated visual images revealing some notable features, and numerical vectors containing some important data such as instantaneous speed  ...  In addition, we can generate a traffic flow behaving diversely to simulate the real traffic flow by using an architecture of fully decentralized training and fully centralized execution without violating  ...  ACKNOWLEDGMENTS This research was partially supported by a Grant-in-Aid for Challenging Research (Exploratory) (19K21572, 2019-2021) from Japan Society for the Promotion of Science (JSPS).  ... 
arXiv:2101.03230v2 fatcat:g372dntmqnhsfgpo2yxhr6fqnq

Segmentation of Echocardiography Based on Deep Learning Model

Helin Huang, Zhenyi Ge, Hairui Wang, Jing Wu, Chunqiang Hu, Nan Li, Xiaomei Wu, Cuizhen Pan
2022 Electronics  
The proposed deep learning network can achieve simultaneous and accurate segmentation of LA, LV, and MV in multi-section echocardiography, laying a foundation for quantitative measurement of clinical parameters  ...  In order to achieve the classification of mitral regurgitation, a deep learning network VDS-UNET was designed to automatically segment the critical regions of echocardiography with three sections of apical  ...  Conclusions In this manuscript, a deep learning segmentation method based on the combination of UNet and the VGG16 network is proposed to achieve simultaneous segmentation of the targets as LA, LV, and  ... 
doi:10.3390/electronics11111714 fatcat:jx5hmx5cdjbv5cqpc2jhm7igpa

Pedestrian Re-Recognition Algorithm Based on Optimization Deep Learning-Sequence Memory Model

Feng-Ping An
2019 Complexity  
Based on the above explanation, this paper proposes a pedestrian re-recognition algorithm based on an optimized LSTM deep learning-sequence memory learning model.  ...  overfitting and parameter initialization in the deep learning model.  ...  Based on the above explanation, this paper proposes a pedestrian re-recognition algorithm based on an optimized LSTM deep learning-sequence memory learning model.  ... 
doi:10.1155/2019/5069026 fatcat:4dez6vvc6ja4dayk7awgtyqwxm

Adaptive Traffic Signal Control Model on Intersections Based on Deep Reinforcement Learning

Duowei Li, Jianping Wu, Ming Xu, Ziheng Wang, Kezhen Hu
2020 Journal of Advanced Transportation  
The model is proposed based on a deep Q-network algorithm that precisely represents the elements associated with the problem: agents, environments, and actions.  ...  The real-time state of traffic, including the number of vehicles and the average speed, at one or more intersections is used as an input to the model.  ...  Acknowledgments is work was supported in part by the NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization under Grant U1709212, and "Research on frontiers of intelligent  ... 
doi:10.1155/2020/6505893 fatcat:sgxrksxvgbgrtekoan6x27qady
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