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Results 1-6 of 6 (Search time: 0.104 seconds).
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  • Planning Approximate Exploration Trajectories for Model-Free Reinforcement Learning in Contact-Rich Manipulation

    • Authors: Hoppe, S.;  Advisor: -;  Participants: Lou, Z.; Hennes, D.; Toussaint, M. (2019)

  • Recent progress in deep reinforcement learning has enabled simulated agents to learn complex behavior policies from scratch, but their data complexity often prohibits real-world applications. The learning process can be sped up by expert demonstrations but those can be costly to acquire. We demonstrate that it is possible to employ model-free deep reinforcement learning combined with planning to quickly generate informative data for a manipulation task. In particular, we use an approximate trajectory optimization approach for global exploration based on an upper confidence bound of the advantage function. The advantage is approximated by a network for Q-learning with separately updated streams for state value and advantage that allows ensembles to approximate model uncertainty for o...
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  • Learning for Personalized Medicine: A Comprehensive Review From a Deep Learning Perspective

    • Authors: Zhang, S.;  Advisor: -;  Participants: Bamakan, S. M. H.; Qu, Q.; Li, S. (2019)

  • With the recent advancements in analyzing high-volume, complex, and unstructured data, modern learning methods are playing an increasingly critical role in the field of personalized medicine. Personalized medicine (i.e., providing tailored medical treatment to individual patients through the identification of common features, including their genetics, inheritance, and lifestyle) has attracted the attention of many researchers in recent years. This paper provides an overview of the research progress in the application of learning methods, with a focus on deep learning in personalized medicine. In particular, three domains of applications are reviewed: drug development, disease characteristic identification, and therapeutic effect prediction. The main objective of this review is to cons...
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  • Information Fusion and Semi-Supervised Deep Learning Scheme for Diagnosing Gear Faults in Induction Machine Systems

    • Authors: Razavi-Far, R.;  Advisor: -;  Participants: Hallaji, E.; Farajzadeh-Zanjani, M.; Saif, M.; Kia, S. H.; Henao, H.; Capolino, G. (2019)

  • There has been an increasing interest in the design of intelligent diagnostic systems for industrial applications. The key requirement in the design of practical diagnostic systems is the ability for decision making in high-dimensional feature spaces, where the prior knowledge about the system states in terms of labels is very limited. Moreover, the problem of diagnosing simultaneous defects is rarely addressed on real industrial applications. This paper aims to develop a semi-supervised deeplearning scheme for diagnosing multiple defects including simultaneous ones in a gearbox directly connected to an induction machine shaft. This scheme consists of two main modules: information fusion and decision making. The former integrates captured multiple sensory streams into a very high...
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  • DeepSTD: Mining Spatio-Temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction

    • Authors: Zheng, Chuanpan;  Advisor: -;  Participants: Fan, Xiaoliang; Wen, Chenglu; Chen, Longbiao; Wang, Cheng; Li, Jonathan (2020)

  • Deep learning techniques have been widely applied to traffic flow prediction, considering underlying routine patterns, and multiple context factors (e.g., time and weather). However, the complex spatio-temporal dependencies between inherent traffic patterns and multiple disturbances have not been fully addressed. In this paper, we propose a two-phase end-to-end deep learning framework, namely DeepSTD to uncover the spatio-temporal disturbances (STD) to predict the citywide traffic flow. In the STD Modeling phase, we propose an STD modeling method to model both the different regional disturbances caused by various region functions and the spatio-temporal propagating effects. In the Prediction phase, we eliminate the STD from the historical traffic flow to enhance the leaning of inher...
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  • Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data

    • Authors: Sattler, F.;  Advisor: -;  Participants: Wiedemann, S.; Müller, K.; Samek, W. (2019)

  • Federated learning allows multiple parties to jointly train a deep learning model on their combined data, without any of the participants having to reveal their local data to a centralized server. This form of privacy-preserving collaborative learning, however, comes at the cost of a significant communication overhead during training. To address this problem, several compression methods have been proposed in the distributed training literature that can reduce the amount of required communication by up to three orders of magnitude. These existing methods, however, are only of limited utility in the federated learning setting, as they either only compress the upstream communication from the clients to the server (leaving the downstream communication uncompressed) or only perform well ...
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  • A Deep Learning Method With Filter Based Feature Engineering for Wireless Intrusion Detection System

    • Authors: Kasongo, S. M.;  Advisor: -;  Participants: Sun, Y. (2019)

  • In recent years, the increased use of wireless networks for the transmission of large volumes of information has generated a myriad of security threats and privacy concerns; consequently, there has been the development of a number of preventive and protective measures including intrusion detection systems (IDS). Intrusion detection mechanisms play a pivotal role in securing computer and network systems; however, for various IDS, the performance remains a major issue. Moreover, the accuracy of existing methodologies for IDS using machine learning is heavily affected when the feature space grows. In this paper, we propose a IDS based on deep learning using feed forward deep neural networks (FFDNNs) coupled with a lter-based feature selection algorithm. The FFDNN-IDS is evaluated usin...
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