Morteza Haghir Chehreghani, Morteza.Chehreghani@chalmers.se Balázs Kulcsár, kulcsar@chalmers.se Sebastien Gros, grosse@chalmers.se *** Chalmers declines to consider all offers of further announcement publishing or other types of support for the recruiting process in connection with this position. ***
2021年1月13日 Eberstein,Jonas Sjöblom,Nikolce Murgovski,Morteza Haghir Chehreghani. 机构：Jonas Sjoblom, Chalmers University of Technology, SE-,
Euhanna Ghadimi. Ericsson AB, Sweden. Verified email at ericsson.com. Optimization Machine Learning Network optimization Wireless Networks. Articles Cited by Public access Co-authors. Title. Sort.
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Chalmers forskningsinformation, projekt och publikationer för Morteza Haghir Chehreghani Chalmers University of Technology - Cited by 575 - Artificial Intelligence - Machine Learning - Data Science Morteza Haghir Chehreghani is this you? claim profile ∙ 0 followers Chalmers University of Technology Research Scientist at Xerox Research Center Europe ( NAVER Mostafa Haghir Chehreghani, Morteza Haghir Chehreghani, Caro Lucas, Masoud Rahgozar: OInduced: An Efficient Algorithm for Mining Induced Patterns From Rooted Ordered Trees. IEEE Trans. Syst. Man Cybern. Part A 41 (5): 1013-1025 (2011) Morteza Haghir Chehreghani is Associate Professor of AI and Machine Learning at Chalmers University of Technology, Department of Computer Science and Engineering, Data Science and AI division. He holds a Ph.D.
Ashkan Panahi ashkan.panahi@chalmers.se Arman Rahbar armanr@chalmers.se Morteza Haghir Chehreghani morteza.chehreghani@chalmers.se Devdatt Dubhashi dubhashi@chalmers.se Department of Computer Science and Engineering Chalmers University of Technology Gothenburg, Sweden Hamid Krim ahk@ncsu.edu Department of Electrical Engineering North Carolina
(If you are not signed up you will not be allowed to enter the examination hall) If you have problems signing up, please send an email to student_office.cse@chalmers.se during the sign-up period! General information: DC Field Value Language; dc.contributor.author: Carlström, Herman-dc.contributor.author: Slottner Seholm, Filip-dc.contributor.department: Chalmers tekniska Frank-WolfeOptimizationforDominantSetClustering CARLJOHNELL DepartmentofComputerScienceandEngineering ChalmersUniversityofTechnologyandUniversityofGothenburg Abstract Request PDF | A Unified Framework for Online Trip Destination Prediction | Trip destination prediction is an area of increasing importance in many applications such as trip planning, autonomous Morteza Haghir Chehreghani Docent på avdelningen för Data Science och AI, Institutionen för data- och informationsteknik. morteza.chehreghani@chalmers.se +46317726415 Hitta till mig Morteza Haghir Chehreghani Docent på avdelningen för Data Science och AI, Institutionen för data- och informationsteknik. morteza.chehreghani@chalmers.se +46317726415 Hitta till mig Morteza Haghir Chehreghani Associate professor, Data Science and AI division, Department of Computer Science and Engineering.
Chalmers University of Technology Examiner: Morteza Haghir Chehreghani, Department of Computer Science and Engineering Master’sThesis2020
If you have any questions please contact academic supervisor Morteza Haghir Chehreghani at morteza.chehreghani@chalmers.se and PhD Student of Computer Science, Chalmers University of Technology - Citerat av 7 A Rahbar, A Panahi, C Bhattacharyya, D Dubhashi, MH Chehreghani. Morteza Haghir Chehreghani (Chalmers). Maria Svedlund (Volvo Cars) maria.svedlund@volvocars.com.
IEEE Trans. Syst. Man Cybern. Part A 41 (5): 1013-1025 (2011)
Carl Johnell, Morteza Haghir Chehreghani Chalmers University of Technology cjohnell@gmail.com, morteza.chehreghani@chalmers.se Abstract We study Frank-Wolfe algorithms – standard, pairwise, and away-steps – for efficient optimization of Dominant Set Clus-tering. We present a unified and computationally efficient
Time Title Student(s) Examiner ; 14:00 - 15:00. Frank-Wolfe Optimization for Dominant Set Clustering (ZOOM: https://chalmers.zoom.us/j/62497622669)
Morteza Haghir Chehreghani, Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden Abstract We propose a hierarchical correlation clustering method that extends the well-known correlation clustering to produce hierarchical clusters.
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7. PhD Student of Computer Science, Chalmers University of Technology - Citerat av 7 A Rahbar, A Panahi, C Bhattacharyya, D Dubhashi, MH Chehreghani. If you have any questions please contact academic supervisor Morteza Haghir Chehreghani at morteza.chehreghani@chalmers.se and PhD Student of Computer Science, Chalmers University of Technology - Citerat av 7 A Rahbar, A Panahi, C Bhattacharyya, D Dubhashi, MH Chehreghani. Morteza Haghir Chehreghani (Chalmers). Maria Svedlund (Volvo Cars) maria.svedlund@volvocars.com.
Part A 41 (5): 1013-1025 (2011)
Time Title Student(s) Examiner ; 14:00 - 15:00. Frank-Wolfe Optimization for Dominant Set Clustering (ZOOM: https://chalmers.zoom.us/j/62497622669)
1 2 3 4 c1 1 c1 2 c2 1 1 2 Input layer Concept layer1 Concept layer2 Output layer Dynamic Network Architectures for Deep Q-Learning Modelling Neurogenesis in
Generation of Driving Scenario Trajectories with Generative Adversarial Networks Andreas Demetriou, Henrik Allsvåg, Sadegh Rahrovani, Morteza Haghir Chehreghani. itsc 2020: 1-6 [doi] Accelerated proximal incremental algorithm schemes for non-strongly convex functions Ashkan Panahi, Morteza Haghir Chehreghani, Devdatt P. Dubhashi. Comparative Study on Optimization Methods for Correlation Clustering Master’s thesis in Computer science and engineering DRIKVY V. CAPPENBERG Department of Computer Science and Engineering
Online Learning for Energy Efficient Navigation using Contextual Information Master’s thesis in Computer science and engineering YONCA YUNATCI
We analyze the general behavior of agglomerative clustering methods, and argue that their strategy yields establishment of a new reliable linkage at each step.
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Chalmers University of Technology - Cited by 549 - Artificial Intelligence - Machine Learning - Data Science
Morteza Haghir Chehreghani. Project with industry: Discovering novel chemical reactions through applying machine learning on knowledge graphs (*) Read more about Active Learning for Artificial Neural Networks (Zoom link: https://chalmers.zoom.us/j/69556986938?pwd=TjFHTGhlNlJmRkRoMzVtNDBIRlBMUT09 password Morteza Haghir Chehreghani's 65 research works with 179 citations and 1,889 reads, including: A Unified Framework for Online Trip Destination Prediction Chalmers University of Technology - Cited by 575 - Artificial Intelligence - Machine Learning - Data Science [1] Morteza Haghir Chehreghani, “Classification with Minimax Distance Measures”, Thirty-First AAAI Conference on Artificial Intelligence (AAAI), 2017. [2] Morteza Haghir Chehreghani, “K-Nearest Neighbor Search and Outlier Detection via Minimax Distances”, SIAM International Conference on Data Mining (SDM), 2016.
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Morteza Haghir Chehreghani Adaptive Information Acquisition and Sequential Decision Making in AI Morteza Haghir Chehreghani is Associate Professor of AI and Machine Learning at Chalmers University of Technology, Department of Computer Science and Engineering, Data Science and AI division. He holds a Ph.D. in Computer Science (AI/Machine Learning group) from ETH Zurich (2014).
1779- 1802, 2020. Mäkeläinen2, Filip Slottner Seholm1, and Morteza Haghir Chehreghani1. 1 Department of Computer Science and Engineering. Chalmers University of View Morteza Haghir Chehreghani's profile on Publons.
Morteza Haghir Chehreghani bor i en bostadsrätt på Doktor Hjorts gata 1 D lgh 1303 i postorten Göteborg i Göteborgs kommun. Området där han bor tillhör Göteborgs Annedals församling. På adressen finns en person folkbokförd, Morteza Haghir Chehreghani (39 år).
We develop a generalized framework wherein different Chalmers University of Technology Examiner: Morteza Haghir Chehreghani, Department of Computer Science and Engineering Master’sThesis2020 Mostafa Haghir Chehreghani, Morteza Haghir Chehreghani, Caro Lucas, Masoud Rahgozar: OInduced: An Efficient Algorithm for Mining Induced Patterns From Rooted Ordered Trees. IEEE Trans. Syst.
in Computer Science (AI/Machine Learning group) from ETH Zurich (2014). Niklas Akerblom˚ 1;3, Yuxin Chen2 and Morteza Haghir Chehreghani3 1Volvo Car Corporation 2The University of Chicago 3Chalmers University of Technology niklas.akerblom@chalmers.se, chenyuxin@uchicago.edu, morteza.chehreghani@chalmers.se Abstract Energy-efficient navigation constitutes an impor-tant challenge in electric vehicles, due to their lim- Add open access links from to the list of external document links (if available). load links from unpaywall.org. Privacy notice: By enabling the option above, your We propose unsupervised representation learning and feature extraction from dendrograms. The commonly used Minimax distance measures correspond to building a dendrogram with single linkage criterion, with defining specific forms of a level function and a distance function over that.