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Onur Dikmen
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Books and Theses
- 2009
- [b1]Onur Dikmen:
Bayesian source modelling for single-channel audio separation (Ses sinyallerinin tek kanaldan ayrıştırılmasında Bayesçi modeller). Boğaziçi University, Turkey, 2009
Journal Articles
- 2022
- [j6]Ece Calikus
, Slawomir Nowaczyk
, Mohamed-Rafik Bouguelia, Onur Dikmen:
Wisdom of the contexts: active ensemble learning for contextual anomaly detection. Data Min. Knowl. Discov. 36(6): 2410-2458 (2022) - 2020
- [j5]Ece Calikus
, Slawomir Nowaczyk
, Anita Pinheiro Sant'Anna, Onur Dikmen:
No free lunch but a cheaper supper: A general framework for streaming anomaly detection. Expert Syst. Appl. 155: 113453 (2020) - 2015
- [j4]Onur Dikmen
, Zhirong Yang
, Erkki Oja:
Learning the Information Divergence. IEEE Trans. Pattern Anal. Mach. Intell. 37(7): 1442-1454 (2015) - 2012
- [j3]Onur Dikmen
, Cédric Févotte:
Maximum Marginal Likelihood Estimation for Nonnegative Dictionary Learning in the Gamma-Poisson Model. IEEE Trans. Signal Process. 60(10): 5163-5175 (2012) - 2010
- [j2]Onur Dikmen
, Ali Taylan Cemgil
:
Gamma Markov Random Fields for Audio Source Modeling. IEEE Trans. Speech Audio Process. 18(3): 589-601 (2010) - 2007
- [j1]Olcay Taner Yildiz
, Onur Dikmen:
Parallel univariate decision trees. Pattern Recognit. Lett. 28(7): 825-832 (2007)
Conference and Workshop Papers
- 2020
- [c12]Lingyun Cheng, Sadhana Sundaresh, Mohamed-Rafik Bouguelia, Onur Dikmen:
Interactive Anomaly Detection Based on Clustering and Online Mirror Descent. IoT Streams/ITEM@PKDD/ECML 2020: 173-186 - 2019
- [c11]Mikko A. Heikkilä, Joonas Jälkö, Onur Dikmen, Antti Honkela:
Differentially Private Markov Chain Monte Carlo. NeurIPS 2019: 4115-4125 - 2017
- [c10]Joonas Jälkö, Antti Honkela, Onur Dikmen:
Differentially Private Variational Inference for Non-conjugate Models. UAI 2017 - 2015
- [c9]Annamaria Mesaros
, Toni Heittola, Onur Dikmen
, Tuomas Virtanen
:
Sound event detection in real life recordings using coupled matrix factorization of spectral representations and class activity annotations. ICASSP 2015: 151-155 - 2014
- [c8]Onur Dikmen
, Ali Taylan Cemgil
:
Sequential Monte Carlo samplers for marginal likelihood computation in multiplicative exponential noise models. SSP 2014: 276-279 - 2013
- [c7]Onur Dikmen
, Annamaria Mesaros
:
Sound event detection using non-negative dictionaries learned from annotated overlapping events. WASPAA 2013: 1-4 - 2012
- [c6]Zhirong Yang, Tele Hao, Onur Dikmen, Xi Chen, Erkki Oja:
Clustering by Nonnegative Matrix Factorization Using Graph Random Walk. NIPS 2012: 1088-1096 - 2011
- [c5]Onur Dikmen
, Cédric Févotte:
Maximum marginal likelihood estimation for nonnegative dictionary learning. ICASSP 2011: 1992-1995 - [c4]Onur Dikmen, Cédric Févotte:
Nonnegative dictionary learning in the exponential noise model for adaptive music signal representation. NIPS 2011: 2267-2275 - 2009
- [c3]Onur Dikmen
, Ali Taylan Cemgil
:
Unsupervised single-channel source separation using bayesian NMF. WASPAA 2009: 93-96 - 2007
- [c2]Ali Taylan Cemgil, Onur Dikmen:
Conjugate Gamma Markov Random Fields for Modelling Nonstationary Sources. ICA 2007: 697-705 - 2003
- [c1]Onur Dikmen, H. Levent Akin
, Ethem Alpaydin
:
Estimating Distributions in Genetic Algorithms. ISCIS 2003: 521-528
Informal and Other Publications
- 2021
- [i8]Ece Calikus, Slawomir Nowaczyk, Mohamed-Rafik Bouguelia, Onur Dikmen:
Wisdom of the Contexts: Active Ensemble Learning for Contextual Anomaly Detection. CoRR abs/2101.11560 (2021) - 2019
- [i7]Mikko A. Heikkilä, Joonas Jälkö, Onur Dikmen, Antti Honkela:
Differentially Private Markov Chain Monte Carlo. CoRR abs/1901.10275 (2019) - [i6]Ece Calikus, Slawomir Nowaczyk
, Anita Pinheiro Sant'Anna, Onur Dikmen:
No Free Lunch But A Cheaper Supper: A General Framework for Streaming Anomaly Detection. CoRR abs/1909.06927 (2019) - 2016
- [i5]Antti Honkela, Mrinal Das, Onur Dikmen, Samuel Kaski:
Efficient differentially private learning improves drug sensitivity prediction. CoRR abs/1606.02109 (2016) - [i4]Joonas Jälkö, Onur Dikmen, Antti Honkela:
Differentially Private Variational Inference for Non-conjugate Models. CoRR abs/1610.08749 (2016) - 2015
- [i3]Ehsan Amid, Onur Dikmen, Erkki Oja:
Optimizing the Information Retrieval Trade-off in Data Visualization Using $α$-Divergence. CoRR abs/1505.05821 (2015) - [i2]Onur Dikmen:
Learning Mixtures of Ising Models using Pseudolikelihood. CoRR abs/1506.02510 (2015) - 2014
- [i1]Onur Dikmen, Zhirong Yang, Erkki Oja:
Learning the Information Divergence. CoRR abs/1406.1385 (2014)
Coauthor Index
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