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Inspired by the great success of deep network in image retrieval, speech recognition, and computer vision [ 13– 21], this paper proposes a deep model framework to deal with the above issues.
Systems of this kind are mostly applicable to text-to-text generation tasks such as text summarisation [23], simplification [24] and others, but may also be embedded in a deeper generation framework [1]. Figure 1 shows the system architecture, in which grey boxes represent its three main modules: symbolic pre-processing, symbolic over-generation and statistical candidate selection.
In this paper, we adopt a deep learning framework for the illumination estimation problem.
This paper proposes a deep learning framework for 3D face reconstruction.
In this work we present a deep learning framework for video compressive sensing.
This paper proposes a novel echo state network (ESN) architecture in a deep learning framework for time series prediction.
We propose a deep boosting framework based on layer-by-layer joint feature boosting and dictionary learning.
Caffe is a deep learning framework made with expression, speed, and modularity in mind, developed by Berkeley AI Research BAIRR) and by community contributors.
They were recognized for their paper describing Caffe, or Convolutional Architecture for Fast Feature Embedding, a deep learning framework that can be used to build multimedia analysis tools.
DeepSolar is a deep learning framework that analyzes satellite imagery to identify the GPS locations and sizes of solar photovoltaic (PV) panels.
It is based on a deep learning framework with a large deep neural network (DNN) consisting of subnets designed from different perspectives.
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