活动简介
In the recent years, deep learning methods have emerged as a powerful machine learning method for many fields. Deep learning methods are different from all traditional approaches. They automatically learn features from raw pixels directly and in a fast way more complex models comparing to shallow ones using the manually designed features. During the past several years, deep learning was successfully applied to a lot of computer vision fields such as autonomous vehicles, speech recognition, and medical imaging task. The fundamental of their success lies on powerful parallel computational power of GPUs. The special session aims to present works relating to the design and use of deep learning in practical applications. This special session exhibits the number of trends and the challenges of the use of deep learning methods in practical applications. In addition, it is expected to gather software developers, specialist researchers and users from diverse fields.
征稿信息
重要日期
征稿范围
The scope of the PADL 2017 includes, but is not limited to the following topics:
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Emerging applications of deep learning such as self-driving cars, speech recognition, face recognition, and medical imaging;
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Application of deep learning in data representation and analysis, including recognition, understanding, detection, segmentation, retrieval, restoration, super-resolution, and compression;
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Distributed computing, GPUs and new hardware for deep learning research;
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Deep learning algorithms and applications including usage the combinations of FPGA, CPU, and GPU;
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Comparisons of FPGA, CPU, and GPU;
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Deep learning model selection;
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Deep learning software frameworks based on CUDA and GPU such as Digit, Python, Matlab, Deeplearning4j, Torch, Theano, TensorFlow, Caffe, Paddle, MxNet, and Keras for applications;
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Deep learning hardware architecture for applications;
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Deep learning algorithms applicable on large-scale data;
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Deep learning on mobile platform
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重要日期
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会议日期
07月03日
2017
至07月05日
2017
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03月01日 2017
初稿截稿日期
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04月15日 2017
初稿录用通知日期
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07月05日 2017
注册截止日期
主办单位
联系方式
- Assoc. Prof. Ayşegül UÇAR
- ag******@firat.edu.tr
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