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

of Kernel and Neural Embeddings: Optimization and Generalization. A Rahbar, E Jorge, D Dubhashi, MH Chehreghani. arXiv preprint arXiv:1905.05095, 2019.

Morteza Haghir Chehreghani, Alberto Giovanni Busetto, Joachim M. Buhmann R (c; X ) = P i kx i c( )k 2. In this case, the potential h i;c (i) = kx i c( )k 2 corresponds to squared distance between data vector and centroid. For non-factorial mod-els, however, computing the potentials is not as straightfor-ward. In the method section, we explain

ETH Zurich morteza.chehreghani@inf.ethz.ch. ETH Zurich and CC-SPMD, Zurich . Morteza Haghir Chehreghani c 2012 J.M. Buhmann, M.H. Chehreghani, M. Frank & A.P. Streich. Page 2. Buhmann Chehreghani Frank Streich. We formulate Ludwig M. Busse · Morteza Haghir Chehreghani · Joachim M Buhmann. Sorting algorithms like MergeSort or BubbleSort order items according to some criterion.

Morteza Haghir Chehreghani. Department of Computer Engineering, Sharif University of Technology, Tehran, Iran, Mostafa Haghir Chehreghani. Department of Computer Engineering, Sharif University of Technology, Tehran, Iran, Hassan Abolhassani. Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

A Rahbar, E Jorge, D Dubhashi, MH Chehreghani. arXiv preprint arXiv:1905.05095, 2019. If you have any questions please contact academic supervisor Morteza Haghir Chehreghani at morteza.chehreghani@chalmers.se and Morteza Haghir Chehreghani (Chalmers). Maria Svedlund (Volvo Cars) maria.svedlund@volvocars.com.

Mostafa Haghir Chehreghani, Morteza Haghir Chehreghani Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 10772, p. 466-478

2007 12/21/2018 ∙ by Morteza Haghir Chehreghani, et al. ∙ 0 ∙ share read it Novel Adaptive Algorithms for Estimating Betweenness, Coverage and k-path Centralities Alberto Giovanni Busetto 1;2, Morteza Haghir Chehreghani , Joachim M. Buhmann 1 Department of Computer Science, ETH Zurich, Zurich, Switzerland 2 Competence Center for Systems Physiology and Metabolic Diseases, Zurich, Switzerland ABSTRACT Models can be seen as mathematical tools aimed at pre-diction. The fundamental modeling question is: which 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- 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 CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We consider sorting data in noisy conditions. Whereas sorting itself is a well studied topic, ordering items when the comparisons between objects can suffer from noise is a rarely addressed question. Morteza Haghir Chehreghani morteza.chehreghani@inf.ethz.ch Mario Frank mfrank@berkeley.edu Andreas P. Streich andreas.streich@alumni.ethz.ch Department of Computer 2020-07-28 · We propose a unified deep learning framework for generation and analysis of driving scenario trajectories, and validate its effectiveness in a principled way. In order to model and generate scenarios of trajectories with different length, we develop two approaches.

1779-1802 . Artikel i vetenskaplig tidskrift 2020. Unsupervised representation learning with Minimax distance measures. Morteza Haghir Chehreghani.
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Morteza Haghir Chehreghani . Docent vid Chalmers, Data- och informationsteknik, Data Science. Andra projekt Forskning. Nikolce Murgovski . Docent vid Chalmers Morteza Haghir Chehreghani1 · Mostafa Haghir Chehreghani2 Received: 11 November 2019 / Revised: 11 May 2020 / Accepted: morteza.chehreghani@chalmers.se [1] Morteza Haghir Chehreghani, “Classification with Minimax Distance Measures”, Thirty-First AAAI Conference on Artificial Intelligence (AAAI), 2017.

ETH Zurich morteza.chehreghani@inf.ethz.ch. ETH Zurich and CC-SPMD, Zurich . Morteza Haghir Chehreghani c 2012 J.M. Buhmann, M.H. Chehreghani, M. Frank & A.P. Streich. Page 2.
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Mostafa Haghir Chehreghani, Morteza Haghir Chehreghani Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 10772, p. 466-478

In this case, the potential h i;c (i) = kx i c( )k 2 corresponds to squared distance between data vector and centroid. For non-factorial mod-els, however, computing the potentials is not as straightfor-ward.

Morteza Haghir Chehreghani. Docent på avdelningen för Data Science och AI, Institutionen för data- och informationsteknik.

In this paper, we present OInduced, a novel and efﬁcient algorithm for ﬁnding frequent ordered induced tre e patterns. Download PDF: Sorry, we are unable to provide the full text but you may find it at the following location(s): https://www.research-collectio (external link) 2 Morteza Haghir Chehreghani, Mostafa Haghir Chehreghani world networks, the distance between each pair of vertices is proportional to the loga-rithm of the number of vertices in the network. However, the model produces an unre-alistic degree distribution. The Barabasi-Albert (BA) model, proposed by … The rapid increase of information on the web makes it necessary to improve information management techniques. One of the most important techniques is clustering web data. In this paper, we propose Morteza Haghir Chehreghani Docent på avdelningen för Data Science och AI, Institutionen för data- och informationsteknik.

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