| Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting B Lim, SO Arik, N Loeff, T Pfister International Journal of Forecasting, 2021 | 1844 | 2021 |
| TabNet: Attentive Interpretable Tabular Learning SO Arik, T Pfister AAAI Conference on Artificial Intelligence (AAAI), 2021 | 1617 | 2021 |
| 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 | 928* | 2018 |
| 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 | 861* | 2017 |
| Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spotting SO Arik, M Kliegl, R Child, J Hestness, A Gibiansky, C Fougner, ... Interspeech, 2017 | 743 | 2017 |
| 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 | 655* | 2017 |
| Neural Voice Cloning with a Few Samples SO Arik, J Chen, K Peng, W Ping, Y Zhou Neural Information Processing Systems (NeurIPS), 2018 | 478 | 2018 |
| On Completeness-Aware Concept-Based Explanations in Deep Neural Networks CK Yeh, B Kim, SO Arik, CL Li, T Pfister, P Ravikumar Neural Information Processing Systems (NeurIPS), 2020 | 379 | 2020 |
| Evaluation of Individual and Ensemble Probabilistic Forecasts of COVID-19 Mortality in the United States EY Cramer, EL Ray, VK Lopez, J Bracher, A Brennen, ... Proceedings of the National Academy of Sciences 119 (15), e2113561119, 2022 | 335* | 2022 |
| Fully Automated Quantitative Cephalometry Using Convolutional Neural Networks SÖ Arık, B Ibragimov, L Xing Journal of Medical Imaging 4 (1), 014501-014501, 2017 | 280 | 2017 |
| Data Valuation using Reinforcement Learning J Yoon, SO Arik, T Pfister International Conference on Machine Learning (ICML), 2020 | 233 | 2020 |
| Consistency-based Semi-Supervised Active Learning: Towards Minimizing Labeling Cost M Gao, Z Zhang, G Yu, SO Arik, LS Davis, T Pfister European Conference on Computer Vision (ECCV), 2020 | 226 | 2020 |
| Tsmixer: An All-MLP Architecture for Time Series Forecasting SA Chen, CL Li, N Yoder, SO Arik, T Pfister Transactions on Machine Learning Research (TMLR), 2023 | 213 | 2023 |
| Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual Understanding Z Zhang, H Zhang, L Zhao, T Chen, SO Arik, T Pfister AAAI Conference on Artificial Intelligence (AAAI), 2022 | 184 | 2022 |
| 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 | 183 | 2013 |
| MIMO Signal Processing for Mode-Division Multiplexing SO Arik, J Kahn, K Ho IEEE Signal Processing Magazine 31 (2), 25-34, 2014 | 169 | 2014 |
| 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 | 167 | 2020 |
| TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting D Cao, F Jia, SO Arik, T Pfister, Y Zheng, W Ye, Y Liu International Conference on Learning Representations (ICLR), 2023 | 151 | 2023 |
| Fast Spectrogram Inversion using Multi-Head Convolutional Neural Networks SO Arik, H Jun, G Diamos IEEE Signal Processing Letters, 2018 | 147 | 2018 |
| 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 | 143 | 2014 |
Tomas PfisterHead of AI Research @ Google CloudOverená e-mailová adresa na: google.com