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Protein secondary structure prediction (PSSP) is a fundamental task in protein science and computational biology, and it can be used to understand protein 3-dimensional (3-D) structures, further, to learn their biological functions. In the past decade, a large number of methods have been proposed for PSSP. In order to learn the latest progress of PSSP, this paper provides a survey on the development...
The deployments of deep neural network models on mobile or embedded devices have been challenged due to two main reasons: 1) the large model size for storage, and 2) the large memory bandwidth for inference. To address these issues, this paper develops a deep neural network compression framework to reduce the resource usage for efficient visual inference. By reviewing the trained deep model, we propose...
In this paper we investigate the image aesthetics classification problem, aka, automatically classifying an image into low or high aesthetic quality, which is quite a challenging problem beyond image recognition. Deep convolutional neural network (DCNN) methods have recently shown promising results for image aesthetics assessment. Currently, a powerful inception module is proposed which shows very...
To reduce patient's dose, few-view CT reconstruction promises to be a good attempt. The key to better reconstruction is the sparse view artifacts. In recent years, DL(deep learing) has attracted a lot of attention because its outstanding performance in image processing. We propose a deep learning method for few-view CT reconstuction. Our method directly learns an end-to-end mapping between the full-view/few-view...
A new acoustic model based on deep neural network (DNN) has been introduced recently and outperforms the conventional Gaussian mixture model (GMM) in speech recognition on several tasks. However, the number of parameters required by a DNN model is much larger than that of its counterpart. The excessive cost of computation cumbers the implementation of DNN in many scenarios. In this paper, a DNN-based...
Configuring a million-core parallel system at boot time is a difficult process when the system has neither specialised hardware support for the configuration process nor a preconfigured default state that puts it in operating condition. SpiNNaker is a parallel Chip Multiprocessor (CMP) system for neural network (NN) simulation. Where most large CMP systems feature a sideband network to complete the...
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