CASIA OpenIR  > 毕业生  > 博士学位论文
Thesis Advisor王亮
Degree Grantor中国科学院大学
Place of Conferral北京
Other Abstract
Along with the development of big data and high performance computing, the major drawbacks of conventional deep neural networks in terms of overfitting and high computational
complexity have been largely overcame。Since the year of 2006, the powerful capability of representation learning of deep neural networks has been uncovered, which gradually becomes a new research area, namely deep learning. Deep learning has promoted fast developments of many related ares, including artificial intelligence, computer vision, pattern recognition, natural language processing, speech processing and robotics, and
drawn much attention from government, industry, university and research. In this thesis,
we focus on developing novel deep learning models and applying them to various pattern
recognition applications. The details are illustrated as follows:
1,By formulating the multi-label learning problem as a multi-task problem, we propose
a deep multi-task conditional Boltzmann machine for unconstrained multimodal learning. In this framework, we can jointly deal with missing modality generation, multimodal fusion and label co-occurrence modeling.
2,By introducing class label information into conventional high-order Boltzmann machines, and studying the corresponding discriminative learning algorithms, we propose new deep conditional high-order Boltzmann machines for accurately measuring the similarity relation between pairwise data.
3,Through replacing all the full connections with weight-sharing convolutions, we propose a fully convolutional version of recurrent neural network, which can reduce the
number of learning parameters from millions to several thousand. In the application
of video super-resolution, the model can achieve better performance and much more
fast speed than existing methods.
4,We propose a selective multimodal long short memory network for image and sentence matching. The model can selectively attend to pairwise salient semantic instances, and dynamically measure their local similarities, as well as aggregate all of them to obtain the global similarity. The model achieves good performance on two publicly available datasets.
Document Type学位论文
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
黄岩. 深度学习新模型及其应用研究[D]. 北京. 中国科学院大学,2017.
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