| Sound event detection in real life recordings using coupled matrix factorization of spectral representations and class activity annotations A Mesaros, T Heittola, O Dikmen, T Virtanen 2015 IEEE international conference on acoustics, speech and signal …, 2015 | 141 | 2015 |
| Clustering by nonnegative matrix factorization using graph random walk Z Yang, T Hao, O Dikmen, X Chen, E Oja Advances in Neural Information Processing Systems 25, 2012 | 101 | 2012 |
| Gamma Markov random fields for audio source modeling O Dikmen, AT Cemgil IEEE Transactions on Audio, Speech, and Language Processing 18 (3), 589-601, 2009 | 64 | 2009 |
| Differentially private variational inference for non-conjugate models J Jälkö, O Dikmen, A Honkela arXiv preprint arXiv:1610.08749, 2016 | 60 | 2016 |
| Sound event detection using non-negative dictionaries learned from annotated overlapping events O Dikmen, A Mesaros 2013 IEEE Workshop on Applications of Signal Processing to Audio and …, 2013 | 60 | 2013 |
| Conjugate gamma Markov random fields for modelling nonstationary sources AT Cemgil, O Dikmen International Conference on Independent Component Analysis and Signal …, 2007 | 60 | 2007 |
| Maximum marginal likelihood estimation for nonnegative dictionary learning in the Gamma-Poisson model O Dikmen, C Févotte IEEE Transactions on Signal Processing 60 (10), 5163-5175, 2012 | 51 | 2012 |
| Parallel univariate decision trees OT Yıldız, O Dikmen Pattern Recognition Letters 28 (7), 825-832, 2007 | 50 | 2007 |
| Learning the information divergence O Dikmen, Z Yang, E Oja IEEE transactions on pattern analysis and machine intelligence 37 (7), 1442-1454, 2014 | 42 | 2014 |
| Efficient differentially private learning improves drug sensitivity prediction A Honkela, M Das, A Nieminen, O Dikmen, S Kaski Biology direct 13, 1-12, 2018 | 39 | 2018 |
| Unsupervised single-channel source separation using Bayesian NMF O Dikmen, AT Cemgil 2009 IEEE Workshop on Applications of Signal Processing to Audio and …, 2009 | 36 | 2009 |
| No free lunch but a cheaper supper: A general framework for streaming anomaly detection E Calikus, S Nowaczyk, A Sant’Anna, O Dikmen Expert Systems with Applications 155, 113453, 2020 | 32 | 2020 |
| Differentially private markov chain monte carlo M Heikkilä, J Jälkö, O Dikmen, A Honkela Advances in Neural Information Processing Systems 32, 2019 | 31 | 2019 |
| Prior structures for Time-Frequency energy distributions AT Cemgil, P Peeling, O Dikmen, S Godsill 2007 IEEE Workshop on Applications of Signal Processing to Audio and …, 2007 | 25 | 2007 |
| Consistent inference of a general model using the pseudolikelihood method A Mozeika, O Dikmen, J Piili Physical Review E 90 (1), 010101, 2014 | 20 | 2014 |
| Nonnegative dictionary learning in the exponential noise model for adaptive music signal representation O Dikmen, C Févotte Advances in Neural Information Processing Systems 24, 2011 | 14 | 2011 |
| Maximum marginal likelihood estimation for nonnegative dictionary learning O Dikmen, C Févotte 2011 IEEE International Conference on Acoustics, Speech and Signal …, 2011 | 10 | 2011 |
| Inference and parameter estimation in Gamma chains O Dikmen, AT Cemgil Department of Engineering, Univ, 2008 | 9 | 2008 |
| Wisdom of the contexts: active ensemble learning for contextual anomaly detection E Calikus, S Nowaczyk, MR Bouguelia, O Dikmen Data Mining and Knowledge Discovery 36 (6), 2410-2458, 2022 | 8 | 2022 |
| Optimizing the Information Retrieval Trade-off in Data Visualization Using -Divergence E Amid, O Dikmen, E Oja arXiv preprint arXiv:1505.05821, 2015 | 5 | 2015 |
