The Experts below are selected from a list of 66459 Experts worldwide ranked by ideXlab platform
Kunqing Xie - One of the best experts on this subject based on the ideXlab platform.
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Deep Architecture for traffic flow prediction Deep belief networks with multitask learning
IEEE Transactions on Intelligent Transportation Systems, 2014Co-Authors: Wenhao Huang, Haikun Hong, Guojie Song, Kunqing XieAbstract:Traffic flow prediction is a fundamental problem in transportation modeling and management. Many existing approaches fail to provide favorable results due to being: 1) shallow in Architecture; 2) hand engineered in features; and 3) separate in learning. In this paper we propose a Deep Architecture that consists of two parts, i.e., a Deep belief network (DBN) at the bottom and a multitask regression layer at the top. A DBN is employed here for unsupervised feature learning. It can learn effective features for traffic flow prediction in an unsupervised fashion, which has been examined and found to be effective for many areas such as image and audio classification. To the best of our knowledge, this is the first paper that applies the Deep learning approach to transportation research. To incorporate multitask learning (MTL) in our Deep Architecture, a multitask regression layer is used above the DBN for supervised prediction. We further investigate homogeneous MTL and heterogeneous MTL for traffic flow prediction. To take full advantage of weight sharing in our Deep Architecture, we propose a grouping method based on the weights in the top layer to make MTL more effective. Experiments on transportation data sets show good performance of our Deep Architecture. Abundant experiments show that our approach achieved close to 5% improvements over the state of the art. It is also presented that MTL can improve the generalization performance of shared tasks. These positive results demonstrate that Deep learning and MTL are promising in transportation research.
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Deep Architecture for traffic flow prediction Deep belief networks with multitask learning
IEEE Transactions on Intelligent Transportation Systems, 2014Co-Authors: Wenhao Huang, Haikun Hong, Guojie Song, Kunqing XieAbstract:Traffic flow prediction is a fundamental problem in transportation modeling and management. Many existing approaches fail to provide favorable results due to being: 1) shallow in Architecture; 2) hand engineered in features; and 3) separate in learning. In this paper we propose a Deep Architecture that consists of two parts, i.e., a Deep belief network (DBN) at the bottom and a multitask regression layer at the top. A DBN is employed here for unsupervised feature learning. It can learn effective features for traffic flow prediction in an unsupervised fashion, which has been examined and found to be effective for many areas such as image and audio classification. To the best of our knowledge, this is the first paper that applies the Deep learning approach to transportation research. To incorporate multitask learning (MTL) in our Deep Architecture, a multitask regression layer is used above the DBN for supervised prediction. We further investigate homogeneous MTL and heterogeneous MTL for traffic flow prediction. To take full advantage of weight sharing in our Deep Architecture, we propose a grouping method based on the weights in the top layer to make MTL more effective. Experiments on transportation data sets show good performance of our Deep Architecture. Abundant experiments show that our approach achieved close to 5% improvements over the state of the art. It is also presented that MTL can improve the generalization performance of shared tasks. These positive results demonstrate that Deep learning and MTL are promising in transportation research.
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ADMA (2) - Deep Architecture for Traffic Flow Prediction
Advanced Data Mining and Applications, 2013Co-Authors: Wenhao Huang, Haikun Hong, Guojie Song, Kunqing XieAbstract:Traffic flow prediction is a fundamental problem in transportation modeling and management. Many existing approaches fail at providing favorable results duo to 1shallow in Architecture;2hand engineered in features. In this paper, we propose a Deep Architecture consists of two parts: a Deep Belief Network in the bottom and a regression layer on the top. The Deep Belief Network employed here is for unsupervised feature learning. It could learn effective features for traffic flow prediction in an unsupervised fashion which has been examined effective for many areas such as image and audio classification. To the best of our knowledge, this is the first work of applying Deep learning approach to transportation research. Experiments on two types of transportation datasets show good performance of our Deep Architecture. Abundant experiments show that our approach could achieve results over state-of-the-art with near 3% improvements. Good results demonstrate that Deep learning is promising in transportation research.
Bin Fang - One of the best experts on this subject based on the ideXlab platform.
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A hybrid Deep Architecture for robotic grasp detection
Proceedings - IEEE International Conference on Robotics and Automation, 2017Co-Authors: Di Guo, Tao Kong, Fuchun Sun, Huaping Liu, Bin Fang, Ning XiAbstract:— The robotic grasp detection is a great challenge in the area of robotics. Previous work mainly employs the visual approaches to solve this problem. In this paper, a hybrid Deep Architecture combining the visual and tactile sensing for robotic grasp detection is proposed. We have demonstrated that the visual sensing and tactile sensing are complementary to each other and important for the robotic grasping. A new THU grasp dataset has also been collected which contains the visual, tactile and grasp configuration information. The experiments conducted on a public grasp dataset and our collected dataset show that the performance of the proposed model is superior to state of the art methods. The results also indicate that the tactile data could help to enable the network to learn better visual features for the robotic grasp detection task.
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ICRA - A hybrid Deep Architecture for robotic grasp detection
2017 IEEE International Conference on Robotics and Automation (ICRA), 2017Co-Authors: Di Guo, Tao Kong, Fuchun Sun, Huaping Liu, Bin FangAbstract:The robotic grasp detection is a great challenge in the area of robotics. Previous work mainly employs the visual approaches to solve this problem. In this paper, a hybrid Deep Architecture combining the visual and tactile sensing for robotic grasp detection is proposed. We have demonstrated that the visual sensing and tactile sensing are complementary to each other and important for the robotic grasping. A new THU grasp dataset has also been collected which contains the visual, tactile and grasp configuration information. The experiments conducted on a public grasp dataset and our collected dataset show that the performance of the proposed model is superior to state of the art methods. The results also indicate that the tactile data could help to enable the network to learn better visual features for the robotic grasp detection task.
Xiaowei Zhao - One of the best experts on this subject based on the ideXlab platform.
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Large-scale prediction of protein ubiquitination sites using a multimodal Deep Architecture
BMC Systems Biology, 2018Co-Authors: Rui Wang, Lingling Bao, Xiaowei ZhaoAbstract:Abstract Background Ubiquitination, which is also called “lysine ubiquitination”, occurs when an ubiquitin is attached to lysine (K) residues in targeting proteins. As one of the most important post translational modifications (PTMs), it plays the significant role not only in protein degradation, but also in other cellular functions. Thus, systematic anatomy of the ubiquitination proteome is an appealing and challenging research topic. The existing methods for identifying protein ubiquitination sites can be divided into two kinds: mass spectrometry and computational methods. Mass spectrometry-based experimental methods can discover ubiquitination sites from eukaryotes, but are time-consuming and expensive. Therefore, it is priority to develop computational approaches that can effectively and accurately identify protein ubiquitination sites. Results The existing computational methods usually require feature engineering, which may lead to redundancy and biased representations. While Deep learning is able to excavate underlying characteristics from large-scale training data via multiple-layer networks and non-linear mapping operations. In this paper, we proposed a Deep Architecture within multiple modalities to identify the ubiquitination sites. First, according to prior knowledge and biological knowledge, we encoded protein sequence fragments around candidate ubiquitination sites into three modalities, namely raw protein sequence fragments, physico-chemical properties and sequence profiles, and designed different Deep network layers to extract the hidden representations from them. Then, the generative Deep representations corresponding to three modalities were merged to build the final model. We performed our algorithm on the available largest scale protein ubiquitination sites database PLMD, and achieved 66.4% specificity, 66.7% sensitivity, 66.43% accuracy, and 0.221 MCC value. A number of comparative experiments also indicated that our multimodal Deep Architecture outperformed several popular protein ubiquitination site prediction tools. Conclusion The results of comparative experiments validated the effectiveness of our Deep network and also displayed that our method outperformed several popular protein ubiquitination site prediction tools. The source codes of our proposed method are available at https://github.com/jiagenlee/DeepUbiquitylation
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Large-scale prediction of protein ubiquitination sites using a multimodal Deep Architecture.
BMC systems biology, 2018Co-Authors: Rui Wang, Lingling Bao, Xiaowei ZhaoAbstract:Ubiquitination, which is also called "lysine ubiquitination", occurs when an ubiquitin is attached to lysine (K) residues in targeting proteins. As one of the most important post translational modifications (PTMs), it plays the significant role not only in protein degradation, but also in other cellular functions. Thus, systematic anatomy of the ubiquitination proteome is an appealing and challenging research topic. The existing methods for identifying protein ubiquitination sites can be divided into two kinds: mass spectrometry and computational methods. Mass spectrometry-based experimental methods can discover ubiquitination sites from eukaryotes, but are time-consuming and expensive. Therefore, it is priority to develop computational approaches that can effectively and accurately identify protein ubiquitination sites. The existing computational methods usually require feature engineering, which may lead to redundancy and biased representations. While Deep learning is able to excavate underlying characteristics from large-scale training data via multiple-layer networks and non-linear mapping operations. In this paper, we proposed a Deep Architecture within multiple modalities to identify the ubiquitination sites. First, according to prior knowledge and biological knowledge, we encoded protein sequence fragments around candidate ubiquitination sites into three modalities, namely raw protein sequence fragments, physico-chemical properties and sequence profiles, and designed different Deep network layers to extract the hidden representations from them. Then, the generative Deep representations corresponding to three modalities were merged to build the final model. We performed our algorithm on the available largest scale protein ubiquitination sites database PLMD, and achieved 66.4% specificity, 66.7% sensitivity, 66.43% accuracy, and 0.221 MCC value. A number of comparative experiments also indicated that our multimodal Deep Architecture outperformed several popular protein ubiquitination site prediction tools. The results of comparative experiments validated the effectiveness of our Deep network and also displayed that our method outperformed several popular protein ubiquitination site prediction tools. The source codes of our proposed method are available at https://github.com/jiagenlee/DeepUbiquitylation .
Di Guo - One of the best experts on this subject based on the ideXlab platform.
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A hybrid Deep Architecture for robotic grasp detection
Proceedings - IEEE International Conference on Robotics and Automation, 2017Co-Authors: Di Guo, Tao Kong, Fuchun Sun, Huaping Liu, Bin Fang, Ning XiAbstract:— The robotic grasp detection is a great challenge in the area of robotics. Previous work mainly employs the visual approaches to solve this problem. In this paper, a hybrid Deep Architecture combining the visual and tactile sensing for robotic grasp detection is proposed. We have demonstrated that the visual sensing and tactile sensing are complementary to each other and important for the robotic grasping. A new THU grasp dataset has also been collected which contains the visual, tactile and grasp configuration information. The experiments conducted on a public grasp dataset and our collected dataset show that the performance of the proposed model is superior to state of the art methods. The results also indicate that the tactile data could help to enable the network to learn better visual features for the robotic grasp detection task.
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ICRA - A hybrid Deep Architecture for robotic grasp detection
2017 IEEE International Conference on Robotics and Automation (ICRA), 2017Co-Authors: Di Guo, Tao Kong, Fuchun Sun, Huaping Liu, Bin FangAbstract:The robotic grasp detection is a great challenge in the area of robotics. Previous work mainly employs the visual approaches to solve this problem. In this paper, a hybrid Deep Architecture combining the visual and tactile sensing for robotic grasp detection is proposed. We have demonstrated that the visual sensing and tactile sensing are complementary to each other and important for the robotic grasping. A new THU grasp dataset has also been collected which contains the visual, tactile and grasp configuration information. The experiments conducted on a public grasp dataset and our collected dataset show that the performance of the proposed model is superior to state of the art methods. The results also indicate that the tactile data could help to enable the network to learn better visual features for the robotic grasp detection task.
Wenhao Huang - One of the best experts on this subject based on the ideXlab platform.
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Deep Architecture for traffic flow prediction Deep belief networks with multitask learning
IEEE Transactions on Intelligent Transportation Systems, 2014Co-Authors: Wenhao Huang, Haikun Hong, Guojie Song, Kunqing XieAbstract:Traffic flow prediction is a fundamental problem in transportation modeling and management. Many existing approaches fail to provide favorable results due to being: 1) shallow in Architecture; 2) hand engineered in features; and 3) separate in learning. In this paper we propose a Deep Architecture that consists of two parts, i.e., a Deep belief network (DBN) at the bottom and a multitask regression layer at the top. A DBN is employed here for unsupervised feature learning. It can learn effective features for traffic flow prediction in an unsupervised fashion, which has been examined and found to be effective for many areas such as image and audio classification. To the best of our knowledge, this is the first paper that applies the Deep learning approach to transportation research. To incorporate multitask learning (MTL) in our Deep Architecture, a multitask regression layer is used above the DBN for supervised prediction. We further investigate homogeneous MTL and heterogeneous MTL for traffic flow prediction. To take full advantage of weight sharing in our Deep Architecture, we propose a grouping method based on the weights in the top layer to make MTL more effective. Experiments on transportation data sets show good performance of our Deep Architecture. Abundant experiments show that our approach achieved close to 5% improvements over the state of the art. It is also presented that MTL can improve the generalization performance of shared tasks. These positive results demonstrate that Deep learning and MTL are promising in transportation research.
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Deep Architecture for traffic flow prediction Deep belief networks with multitask learning
IEEE Transactions on Intelligent Transportation Systems, 2014Co-Authors: Wenhao Huang, Haikun Hong, Guojie Song, Kunqing XieAbstract:Traffic flow prediction is a fundamental problem in transportation modeling and management. Many existing approaches fail to provide favorable results due to being: 1) shallow in Architecture; 2) hand engineered in features; and 3) separate in learning. In this paper we propose a Deep Architecture that consists of two parts, i.e., a Deep belief network (DBN) at the bottom and a multitask regression layer at the top. A DBN is employed here for unsupervised feature learning. It can learn effective features for traffic flow prediction in an unsupervised fashion, which has been examined and found to be effective for many areas such as image and audio classification. To the best of our knowledge, this is the first paper that applies the Deep learning approach to transportation research. To incorporate multitask learning (MTL) in our Deep Architecture, a multitask regression layer is used above the DBN for supervised prediction. We further investigate homogeneous MTL and heterogeneous MTL for traffic flow prediction. To take full advantage of weight sharing in our Deep Architecture, we propose a grouping method based on the weights in the top layer to make MTL more effective. Experiments on transportation data sets show good performance of our Deep Architecture. Abundant experiments show that our approach achieved close to 5% improvements over the state of the art. It is also presented that MTL can improve the generalization performance of shared tasks. These positive results demonstrate that Deep learning and MTL are promising in transportation research.
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ADMA (2) - Deep Architecture for Traffic Flow Prediction
Advanced Data Mining and Applications, 2013Co-Authors: Wenhao Huang, Haikun Hong, Guojie Song, Kunqing XieAbstract:Traffic flow prediction is a fundamental problem in transportation modeling and management. Many existing approaches fail at providing favorable results duo to 1shallow in Architecture;2hand engineered in features. In this paper, we propose a Deep Architecture consists of two parts: a Deep Belief Network in the bottom and a regression layer on the top. The Deep Belief Network employed here is for unsupervised feature learning. It could learn effective features for traffic flow prediction in an unsupervised fashion which has been examined effective for many areas such as image and audio classification. To the best of our knowledge, this is the first work of applying Deep learning approach to transportation research. Experiments on two types of transportation datasets show good performance of our Deep Architecture. Abundant experiments show that our approach could achieve results over state-of-the-art with near 3% improvements. Good results demonstrate that Deep learning is promising in transportation research.
