| Graphical lasso based model selection for time series A Jung, G Hannak, N Goertz IEEE Signal Processing Letters 22 (10), 1781-1785, 2015 | 63 | 2015 |
| Joint channel estimation and activity detection for multiuser communication systems G Hannak, M Mayer, A Jung, G Matz, N Goertz 2015 IEEE International Conference on Communication Workshop (ICCW), 2086-2091, 2015 | 59 | 2015 |
| Graph signal recovery via primal-dual algorithms for total variation minimization P Berger, G Hannak, G Matz IEEE Journal of Selected Topics in Signal Processing 11 (6), 842-855, 2017 | 52 | 2017 |
| On the convergence of average consensus with generalized Metropolis-Hasting weights V Schwarz, G Hannak, G Matz 2014 IEEE International Conference on Acoustics, Speech and Signal …, 2014 | 40 | 2014 |
| Efficient graph learning from noisy and incomplete data P Berger, G Hannak, G Matz IEEE Transactions on Signal and Information Processing over Networks 6, 105-119, 2020 | 32 | 2020 |
| Performance Analysis of Approximate Message Passing for Distributed Compressed Sensing G Hannak, A Perelli, N Goertz, G Matz, ME Davies IEEE Journal of Selected Topics in Signal Processing 12 (5), 857-870, 2018 | 23 | 2018 |
| Scalable graph signal recovery for big data over networks A Jung, P Berger, G Hannak, G Matz 2016 IEEE 17th International Workshop on Signal Processing Advances in …, 2016 | 16 | 2016 |
| Measurement based evaluation of interference alignment on the Vienna MIMO testbed M Mayer, G Artner, G Hannak, M Lerch, M Guillaud ISWCS 2013; The Tenth International Symposium on Wireless Communication …, 2013 | 15 | 2013 |
| Graph learning based on total variation minimization P Berger, M Buchacher, G Hannak, G Matz 2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018 | 12 | 2018 |
| Generalized approximate message passing for one-bit compressed sensing with AWGN O Musa, G Hannak, N Goertz 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP …, 2016 | 11 | 2016 |
| Efficient graph signal recovery over big networks G Hannak, P Berger, G Matz, A Jung 2016 50th Asilomar Conference on Signals, Systems and Computers, 1839-1843, 2016 | 11 | 2016 |
| Semi-supervised multiclass clustering based on signed total variation P Berger, T Dittrich, G Hannak, G Matz ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 10 | 2019 |
| On the information-theoretic limits of graphical model selection for Gaussian time series G Hannak, A Jung, N Goertz 2014 22nd European Signal Processing Conference (EUSIPCO), 516-520, 2014 | 6 | 2014 |
| Bayesian QAM demodulation and activity detection for multiuser communication systems G Hannak, M Mayer, G Matz, N Goertz 2016 IEEE International Conference on Communications Workshops (ICC), 596-601, 2016 | 4 | 2016 |
| Efficient recovery from noisy quantized compressed sensing using generalized approximate message passing O Musa, G Hannak, N Goertz 2017 IEEE 7th International Workshop on Computational Advances in Multi …, 2017 | 3 | 2017 |
| Fast bayesian signal recovery in compressed sensing with partially unknown discrete prior N Goertz, G Hannak WSA 2017; 21th International ITG Workshop on Smart Antennas, 1-8, 2017 | 3 | 2017 |
| An approach to complex Bayesian-optimal approximate message passing G Hannak, M Mayer, G Matz, N Goertz arXiv preprint arXiv:1511.08238, 2015 | 3 | 2015 |
| Graphical LASSO based model selection for time series A Jung, G Hannak, N Görtz arXiv preprint arXiv:1410.1184, 2014 | 3 | 2014 |
| Coordinate descent accelerations for signal recovery on scale-free graphs based on total variation minimization P Berger, G Hannak, G Matz 2017 25th European Signal Processing Conference (EUSIPCO), 1689-1693, 2017 | 2 | 2017 |
| Bayesian approximate message passing for distributed compressed sensing G Hannák Wien, 2017 | 1 | 2017 |
Gerald MatzInstitute of Telecommunications, Vienna University of TechnologyVerified email at tuwien.ac.at
