Melih Kandemir

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Fred A. HamprechtProfessor, Heidelberg UniversityEmail yang diverifikasi di iwr.uni-heidelberg.de
Manuel HaussmannSyddansk UniversitetEmail yang diverifikasi di imada.sdu.dk
Samuel KaskiProfessor, Aalto University and University of ManchesterEmail yang diverifikasi di aalto.fi
Murat SensoyDepartment of Computer Science, University College LondonEmail yang diverifikasi di ucl.ac.uk
Andreas LookBosch Center for AIEmail yang diverifikasi di de.bosch.com
Jan PetersProfessor for Intelligent Autonomous Systems/TU Darmstadt, Dept. Head/German AI Research Center DFKIEmail yang diverifikasi di ias.tu-darmstadt.de
Barbara RakitschBosch Center for AIEmail yang diverifikasi di de.bosch.com
Arto KlamiAssociate Professor, University of HelsinkiEmail yang diverifikasi di cs.helsinki.fi
Cigdem Gunduz DemirProfessor of Computer Engineering, Koç UniversityEmail yang diverifikasi di ku.edu.tr
Kai PuolamäkiProfessor, University of Helsinki, FinlandEmail yang diverifikasi di helsinki.fi
Hamish FlynnUniversitat Pompeu FabraEmail yang diverifikasi di upf.edu
Gozde Unal, PhD, ProfessorIstanbul Technical University, Department of Computer Engineering / AI & Data EngineeringEmail yang diverifikasi di itu.edu.tr
Riitta HariProf. (emer.) @ Aalto University, School of ARTS & School of SCI; FinlandEmail yang diverifikasi di aalto.fi
Christian WojekZEISSEmail yang diverifikasi di zeiss.com
Jeroen van der LaakRadboud University Medical CenterEmail yang diverifikasi di radboudumc.nl

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Melih Kandemir

Associate Professor of Machine Learning at the University of Southern Denmark

Email yang diverifikasi di imada.sdu.dk - Beranda

Bayesian Inference Neural Stochastic Processes Dynamics Modeling Reinforcement Learning


Judul Urutkan menurut kutipan Urutkan menurut tahun Urutkan menurut judul	Dikutip oleh Dikutip oleh	Tahun
Evidential Deep Learning to Quantify Classification Uncertainty M Sensoy, L Kaplan, M Kandemir Advances in Neural Information Processing Systems (NeurIPS), 3179-3189, 2018	1190	2018
Automatic segmentation of colon glands using object-graphs C Gunduz-Demir, M Kandemir, AB Tosun, C Sokmensuer Medical image analysis 14 (1), 1-12, 2010	194	2010
Computer-aided diagnosis from weak supervision: A benchmarking study M Kandemir, FA Hamprecht Computerized medical imaging and graphics 42, 44-50, 2015	148	2015
An augmented reality interface to contextual information A Ajanki, M Billinghurst, H Gamper, T Järvenpää, M Kandemir, S Kaski, ... Virtual reality 15, 161-173, 2011	143	2011
Object-oriented texture analysis for the unsupervised segmentation of biopsy images for cancer detection AB Tosun, M Kandemir, C Sokmensuer, C Gunduz-Demir Pattern Recognition 42 (6), 1104-1112, 2009	117	2009
Empowering multiple instance histopathology cancer diagnosis by cell graphs M Kandemir, C Zhang, FA Hamprecht Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014: 17th …, 2014	83	2014
Towards brain-activity-controlled information retrieval: Decoding image relevance from MEG signals JP Kauppi, M Kandemir, VM Saarinen, L Hirvenkari, L Parkkonen, A Klami, ... NeuroImage 112, 288-298, 2015	61	2015
Asymmetric Transfer Learning with Deep Gaussian Processes M Kandemir International Conference on Machine Learning, 730-738, 2015	60	2015
Deep Active Learning with Adaptive Acquisition M Haußmann, FA Hamprecht, M Kandemir International Joint Conference on Artificial Intelligence (IJCAI), arXiv …, 2019	51	2019
Multi-task and multi-view learning of user state M Kandemir, A Vetek, M Gönen, A Klami, S Kaski Neurocomputing 139, 97-106, 2014	49	2014
Gaussian process density counting from weak supervision M von Borstel, M Kandemir, P Schmidt, MK Rao, K Rajamani, ... Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016	46	2016
Variational Bayesian Multiple Instance Learning with Gaussian Processes M Haußmann, FA Hamprecht, M Kandemir Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017	43	2017
Digital pathology: Multiple instance learning can detect Barrett's cancer M Kandemir, A Feuchtinger, A Walch, FA Hamprecht 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), 1348-1351, 2014	42	2014
Sampling-Free Variational Inference of Bayesian Neural Nets with Variance Backpropagation M Haussmann, FA Hamprecht, M Kandemir Uncertainty in Artificial Intelligence (UAI), arXiv preprint arXiv:1805.07654, 2019	36*	2019
Inferring object relevance from gaze in dynamic scenes M Kandemir, VM Saarinen, S Kaski Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications …, 2010	25	2010
Instance Label Prediction by Dirichlet Process Multiple Instance Learning. M Kandemir, FA Hamprecht UAI, 380-389, 2014	24	2014
Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes M Haussmann, S Gerwinn, A Look, B Rakitsch, M Kandemir International Conference on Artificial Intelligence and Statistics (AISTATS), 2021	22	2021
Contextual information access with augmented reality A Ajanki, M Billinghurst, T Järvenpää, M Kandemir, S Kaski, M Koskela, ... 2010 IEEE International Workshop on Machine Learning for Signal Processing …, 2010	22	2010
Multiple instance learning: Robust validation on retinopathy of prematurity P Rani, R Elagiri Ramalingam, KT Rajamani, M Kandemir, D Singh Int J Ctrl Theory Appl 9, 451-459, 2016	21	2016
Prediction of active UE number with Bayesian neural networks for self-organizing LTE networks O Narmanlioglu, E Zeydan, M Kandemir, T Kranda 2017 8th International Conference on the Network of the Future (NOF), 73-78, 2017	20	2017

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