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检索条件"机构=Machine Learning and Robotics Laboratory"
57 条 记 录,以下是31-40 订阅
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Portable tracker for neurophysiological research of sport shooting
Portable tracker for neurophysiological research of sport sh...
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Saratov Fall Meeting 2021: Computational Biophysics and Nanobiophotonics
作者: Antipov, V.M. Badarin, A.A. Grubov, V.V. Kazantsev, V.B. Hramov, A.E. Neuroscience and Cognivite Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Universitetskaya Str. 1 Innopolis 420500 Russia Laboratory of Advanced Methods for High-Dimensional Data Analysis Lobachevsky State University of Nizhni Novgorod 23 Gagarin ave. Nizhny Novgorod603950 Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University A. Nevskogo ul. 14 Kaliningrad236016 Russia Neurotechnology Deparment Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod603022 Russia
In this work we present the development process of a wireless portable module. It is developed to record various characteristics during sport shooting, such as automatic detection of the moment of shot and barrel move... 详细信息
来源: 评论
BrainIB: Interpretable Brain Network-based Psychiatric Diagnosis with Graph Information Bottleneck
arXiv
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arXiv 2022年
作者: Zheng, Kaizhong Yu, Shujian Li, Baojuan Jenssen, Robert Chen, Badong National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China The Department of Computer Science Vrije Universiteit Amsterdam Amsterdam and the Machine Learning Group UiT - Arctic University of Norway Tromsø Norway The Machine Learning Group UiT - Arctic University of Norway Tromsø Norway The School of Biomedical Engineering Fourth Military Medical University Xi’an China
Developing a new diagnostic models based on the underlying biological mechanisms rather than subjective symptoms for psychiatric disorders is an emerging consensus. Recently, machine learning-based classifiers using f... 详细信息
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Bistable Dynamics of the Brain Extracellular Matrix in the Presence of Periodically Varying Neuronal Activity  5
Bistable Dynamics of the Brain Extracellular Matrix in the P...
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5th Scientific School on Dynamics of Complex Networks and their Applications, DCNA 2021
作者: Rozhnova, Maiya A. Kazantsev, Victor B. Pankratova, Evgeniya V. Department of Applied Mathematics Institute of Information Technologies Mathematics and Mechanics Lobachevsky State University of Nizhni Novgorod Nizhny Novgorod Russia Neurotechnology Department Lobachevsky State University Nizhny Novgorod Russia Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Neuroscience Research Institute Samara State Medical University Samara Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University Kaliningrad Russia
In this work, the role of the amplitude and frequency of the periodically varying neuronal firing rate in emergence of complicated dynamical modes in recently proposed mathematical model describing the change of extra... 详细信息
来源: 评论
CI-GNN: A Granger Causality-Inspired Graph Neural Network for Interpretable Brain Network-Based Psychiatric Diagnosis
arXiv
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arXiv 2023年
作者: Zheng, Kaizhong Yu, Shujian Chen, Badong National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi’an China Department of Computer Science Vrije Universiteit Amsterdam Amsterdam Netherlands Machine Learning Group UiT - Arctic University of Norway Tromsø Norway
There is a recent trend to leverage the power of graph neural networks (GNNs) for brain-network based psychiatric diagnosis, which, in turn, also motivates an urgent need for psychiatrists to fully understand the deci... 详细信息
来源: 评论
Deep Convolutional Neural Networks with Zero-Padding: Feature Extraction and learning
arXiv
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arXiv 2023年
作者: Han, Zhi Liu, Baichen Lin, Shao-Bo Zhou, Ding-Xuan State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Center of Intelligent Decision-Making and Machine Learning School of Management Xi’an Jiaotong University Xi’an China School of Mathematics and Statistics University of Sydney SydneyNSW2006 Australia
This paper studies the performance of deep convolutional neural networks (DCNNs) with zero-padding in feature extraction and learning. After verifying the roles of zero-padding in enabling translation-equivalence, and... 详细信息
来源: 评论
Bursting activity interplay in modular neural networks in vitro  3
Bursting activity interplay in modular neural networks in vi...
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3rd International Conference "Neurotechnologies and Neurointerfaces", CNN 2021
作者: Gladkov, Arseniy Pigareva, Yana Kolpakov, Vladimir Mukhina, Irina Bukatin, Anton Kazantsev, Victor Pimashkin, Alexey Cell Technology Department Central Research Laboratory Privolzhsky Research Medical University Nizhny Novgorod Russia Neurotechnology Department Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Laboratory of Renewable Energy Sources Alferov Saint-Petersburg National Research Academic University of the Russian Academy of Sciences Saint-Petersburg Russia Laboratory of Bio and Chemosensor Microsystems Institute for Analytical Instrumentation of the RAS Saint-Petersburg Russia Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University Kaliningrad Russia NeurotechnologyDepartment Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia
Neural networks modularity is a major challenge to study spiking pattern propagation and information processing in the brain. Sparse and heterogeneous connectivity between network modules defines spiking activity gene... 详细信息
来源: 评论
A multi-objective deep reinforcement learning algorithm for spatio-temporal latency optimization in mobile IoT-enabled edge computing networks
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Simulation Modelling Practice and Theory 2025年 143卷
作者: Parisa Khoshvaght Amir Haider Amir Masoud Rahmani Farhad Soleimanian Gharehchopogh Ferzat Anka Jan Lansky Mehdi Hosseinzadeh Institute of Research and Development Duy Tan University Da Nang Vietnam School of Engineering & Technology Duy Tan University Da Nang Vietnam Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India Department of AI and Robotics Sejong University Seoul 05006 Republic of Korea Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Engineering Ur. C. Islamic Azad University Urmia Iran Data Science Application and Research Center (VEBIM) Fatih Sultan Mehmet Vakif University Istanbul Türkiye Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Pattern Recognition and Machine Learning Laboratory School of Computing Gachon University Seongnam Republic of Korea
The rapid increase in Mobile Internet of Things (IoT) devices requires novel computational frameworks. These frameworks must meet strict latency and energy efficiency requirements in Edge and Mobile Edge Computing (ME...
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Model of ‘implant-host’ neural circuits in a microfluidic chip in vitro  8
Model of ‘implant-host’ neural circuits in a microfluidic ...
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8th International School and Conference "Saint Petersburg OPEN 2021" on Optoelectronics, Photonics, Engineering and Nanostructures, SPbOPEN 2021
作者: Kolpakov, V.N. Pigareva, Y.I. Gladkov, A.A. Bukatin, A.S. Kazantsev, V.B. Mukhina, I.V. Pimashkin, A.S. Neurotechnology Department Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod603950 Russia Cell Technology Department Central Research Laboratory Privolzhsky Research Medical University Nizhny Novgorod603005 Russia Laboratory of Renewable Energy Sources Alferov Saint-Petersburg National Research Academic University of the Russian Academy of Sciences Saint-Petersburg194021 Russia Laboratory of Bio and Chemosensor Microsystems Institute for Analytical Instrumentation of the RAS Saint-Petersburg198095 Russia Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University 1 Universitetskaya Str. Innopolis420500 Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University 14 Nevsky Str. Kaliningrad236016 Russia
In this study, we developed a new model of neuronal cells plating into a developed neural network to study functional integration using microfluidic methods. The integration was modeled in a three-chamber microfluidic... 详细信息
来源: 评论
A Structured Prediction Approach for Robot Imitation learning
arXiv
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arXiv 2023年
作者: Duan, Anqing Batzianoulis, Iason Camoriano, Raffaello Rosasco, Lorenzo Pucci, Daniele Billard, Aude Robotics and Machine Intelligence Laboratory The Hong Kong Polytechnic University Hong Kong Learning Algorithms and Systems Laboratory École Polytechnique Fédérale de Lausanne Lausanne Switzerland Visual And Multimodal Applied Learning Laboratory Politecnico di Torino Turin Italy DIBRIS Università degli Studi di Genova Genoa Italy Laboratory for Computational and Statistical Learning IIT@MIT Istituto Italiano di Tecnologia Massachusetts Institute of Technology CambridgeMA United States Center Università di Genova Genoa Italy Dynamic Interaction Control research line Italian Institute of Technology Genoa Italy
We propose a structured prediction approach for robot imitation learning from demonstrations. Among various tools for robot imitation learning, supervised learning has been observed to have a prominent role. Structure... 详细信息
来源: 评论
Relevance attack on detectors
arXiv
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arXiv 2020年
作者: Chen, Sizhe He, Fan Huang, Xiaolin Zhang, Kun Department of Automation The Institute of Medical Robotics The MOE Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China Philosophy Department and Machine Learning Department Carnegie Mellon University 5000 Forbes Ave PittsburghPA15213 United States
This paper focuses on high-transferable adversarial attacks on detectors, which are hard to attack in a black-box manner, because of their multiple-output characteristics and the diversity across architectures. To pur... 详细信息
来源: 评论