| Deep Voice: Real-time neural text-to-speech SO Arik, M Chrzanowski, A Coates, G Diamos, A Gibiansky, Y Kang, X Li, ... International Conference on Machine Learning (ICML) 2017, 2017 | 381* | 2017 |
| Deep Voice 3: Scaling text-to-speech with convolutional sequence learning W Ping, K Peng, A Gibiansky, SO Arik, A Kannan, S Narang, J Raiman, ... International Conference on Learning Representations (ICLR) 2018, 2018 | 370* | 2018 |
| Deep Voice 2: Multi-speaker neural text-to-speech SO Arik, G Diamos, A Gibiansky, J Miller, K Peng, W Ping, J Raiman, ... Neural Information Processing Systems (NeurIPS) 2017, 2017 | 292* | 2017 |
| Neural voice cloning with a few samples SO Arik, J Chen, K Peng, W Ping, Y Zhou Neural Information Processing Systems (NeurIPS) 2018, 2018 | 136 | 2018 |
| Effect of mode coupling on signal processing complexity in mode-division multiplexing SÖ Arık, D Askarov, JM Kahn Journal of Lightwave Technology 31 (3), 423-431, 2013 | 129 | 2013 |
| MIMO signal processing for mode-division multiplexing SO Arik, J Kahn, K Ho IEEE Signal Processing Magazine 31 (2), 25-34, 2014 | 116 | 2014 |
| High-dimensional modulation for coherent optical communications systems DS Millar, T Koike-Akino, SÖ Arık, K Kojima, K Parsons, T Yoshida, ... Optics express 22 (7), 8798-8812, 2014 | 114 | 2014 |
| Adaptive frequency-domain equalization in mode-division multiplexing systems SÖ Arık, D Askarov, JM Kahn Journal of Lightwave Technology 32 (10), 1841 - 1852, 2014 | 105 | 2014 |
| Convolutional recurrent neural networks for small-footprint keyword spotting SO Arik, M Kliegl, R Child, J Hestness, A Gibiansky, C Fougner, ... Interspeech 2017, 2017 | 97* | 2017 |
| Fully automated quantitative cephalometry using convolutional neural networks SÖ Arik, B Ibragimov, L Xing Journal of Medical Imaging 4 (1), 014501, 2017 | 64 | 2017 |
| Coupled-core multi-core fibers for spatial multiplexing SÖ Arik, JM Kahn IEEE Photonics Technology Letters 25 (21), 2054 - 2057, 2013 | 54 | 2013 |
| Group delay management and multi-input multi-output signal processing in mode-division multiplexing systems SO Arik, KP Ho, J Kahn Journal of Lightwave Technology, 2015 | 49 | 2015 |
| Fast spectrogram inversion using multi-head convolutional neural networks SO Arik, H Jun, G Diamos IEEE Signal Processing Letters, 2018 | 46 | 2018 |
| Resource-efficient neural architect Y Zhou, S Ebrahimi, SÖ Arık, H Yu, H Liu, G Diamos arXiv preprint arXiv:1806.07912, 2018 | 39 | 2018 |
| Optical network scaling: Roles of spectral and spatial aggregation SÖ Arık, KP Ho, JM Kahn Optics express 22 (24), 29868-29887, 2014 | 37 | 2014 |
| TabNet: Attentive interpretable tabular learning SO Arik, T Pfister AAAI Conference on Artificial Intelligence (AAAI), 2021 | 34 | 2021 |
| Delay spread reduction in mode-division multiplexing: mode coupling vs. delay compensation SO Arik, KP Ho, J Kahn Journal of Lightwave Technology 33 (21), 4504 - 4512, 2015 | 29 | 2015 |
| Temporal fusion transformers for interpretable multi-horizon time series forecasting B Lim, SO Arik, N Loeff, T Pfister arXiv preprint arXiv:1912.09363, 2019 | 24 | 2019 |
| Direct-detection mode-division multiplexing in modal basis using phase retrieval SÖ Arik, JM Kahn Optics letters 41 (18), 4265-4268, 2016 | 24 | 2016 |
| Distilling effective supervision from severe label noise Z Zhang, H Zhang, SO Arik, H Lee, T Pfister Conference on Computer Vision and Pattern Recognition (CVPR) 2020, 2020 | 23 | 2020 |
Joseph M. KahnProfessor of Electrical Engineering, E. L. Ginzton Laboratory, Stanford UniversityVerified email at ee.stanford.edu
