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. Therefore, we extend this method to arbitrary dendrograms. We develop a generalized framework wherein different

Morteza Haghir Chehreghani Docent på avdelningen för Data Science och AI, Institutionen för data- och informationsteknik. morteza.chehreghani@chalmers.se +46317726415 Hitta till mig

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. We then investigate Chalmers University of Technology University of Gothenburg Gothenburg,Sweden2019. Supervisor: Morteza Haghir Chehreghani, Department of Computer Science and 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 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- Attaining Higher Quality for Density Based Algorithms Morteza Haghir Chehreghani, Hassan Abolhassani, Mostafa Haghir Chehreghani.

1. Guldmedaljong 18 karat
2. Forbudsmarke cykel

Proceedings of ICML Workshop on Unsupervised and Transfer Learning, 51-64, 2012. 18, 2012. Chalmers University of Technology (10/2020). - Statistics Colloquium (3) Niklas Akerblom, Yuxin Chen, Morteza Haghir Chehreghani. An Online Learning  Morteza Haghir Chehreghani. Docent vid Data- och informationsteknik. Länk till personlig sida.

Morteza Haghir Chehreghani Data Science. Chalmers. There might be more projects where Morteza Haghir Chehreghani participates, but you have to be logged in as a Chalmers employee to see them.

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.

If you have any questions please contact academic supervisor Morteza Haghir Chehreghani at morteza.chehreghani@chalmers.se and 

Joachim M. Buhmann.

Sebastien Gros Automatic Control. Chalmers AI Research Centre (CHAIR) Chalmers. 2019–2022. Modelling and optimization of energy management systems for plug-in hybrid vehicles. Read more about Frank-Wolfe Optimization for Dominant Set Clustering (ZOOM: https://chalmers.zoom.us/j/62497622669); Active Learning for Artificial Neural Networks Morteza Haghir Chehreghani. Project with industry: Discovering novel chemical reactions through applying machine learning on knowledge graphs (*) Read more about Project with industry: Discovering novel chemical reactions through applying machine learning on knowledge graphs (*) Morteza Haghir Chehreghani's 65 research works with 179 citations and 1,889 reads, including: A Unified Framework for Online Trip Destination Prediction [1] Morteza Haghir Chehreghani, “Classification with Minimax Distance Measures”, Thirty-First AAAI Conference on Artificial Intelligence (AAAI), 2017.
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An Online Learning  Morteza Haghir Chehreghani. Docent vid Data- och informationsteknik. Länk till personlig sida. Källa: chalmers.se. Kontaktuppgifter.

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Chalmers forskningsinformation, projekt och publikationer för Morteza Haghir Chehreghani

Morteza Haghir Chehreghani Data Science. Chalmers. There might be more projects where Morteza Haghir Chehreghani participates, but you have to be logged in as a Chalmers employee to see them. Morteza Haghir Chehreghani Project with industry: Discovering novel chemical reactions through applying machine learning on knowledge graphs (*) Read more about Project with industry: Discovering novel chemical reactions through applying machine learning on knowledge graphs (*) Morteza Haghir Chehreghani Docent på avdelningen för Data Science och AI, Institutionen för data- och informationsteknik. morteza.chehreghani@chalmers.se +46317726415 Hitta till mig Read more about Active Learning for Artificial Neural Networks (Zoom link: https://chalmers.zoom.us/j/69556986938?pwd=TjFHTGhlNlJmRkRoMzVtNDBIRlBMUT09 password Morteza Haghir Chehreghani's research while affiliated with Chalmers University of Technology and other places. Morteza Haghir Chehreghani's research. while affiliated with.

Morteza Haghir Chehreghani. M. orteza Ha. ghir. Chehreghani. I am an Associate Professor in Machine Learning and Artificial Intelligence at the Department of Computer Science and Engineering, Chalmers University of Technology. I obtained my Ph.D. from ETH Zurich in 2014, where I worked at the Machine Learning Institute under the supervision of

Morteza Haghir Chehreghani (academic supervisor) morteza.chehreghani@chalmers.se; Sadegh Rahrovani, (industrial supervisor) sadegh.rahrovani@volvocars.com; Martin Magnusson (Group manager at VolvoCars), martin.m.magnusson@volvocars.com (*) The project is taken and will be conducted by students during spring 2021 Erik Thiel, Morteza Haghir Chehreghani, Devdatt Dubhashi THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES … 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) 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.

Subscribe to our newsletter. 12/20/2018 ∙ by Morteza Haghir Chehreghani, et al. ∙ 0 ∙ share read it Efficient Online Learning for Optimizing Value of Information: Theory and Application to Interactive Troubleshooting 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. Therefore, we extend this method to arbitrary dendrograms. 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.