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ORCIDSercan Ö. Arik
Sercan Ömer Arik – Sercan Ö. Arık
Person information
- unicode name: Sercan Ö. Arık
- affiliation: Google Cloud AI, Sunnyvale, CA, USA
- affiliation (PhD 2016): Stanford University, Department of Electrical Engineering, CA, USA
- affiliation: Bilkent University, Depertment of Electrical and Electronics Engineering, Ankara, Turkey
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2020 – today
- 2023
[j9]Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li, Sercan Ö. Arik, Tomas Pfister:
SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch. Trans. Mach. Learn. Res. 2023 (2023)- [c30]Ruoxi Sun, Chun-Liang Li, Sercan Ö. Arik, Michael W. Dusenberry, Chen-Yu Lee, Tomas Pfister:
Neural Spline Search for Quantile Probabilistic Modeling. AAAI 2023: 9927-9934 - [c29]Xingchen Wan, Ruoxi Sun, Hanjun Dai, Sercan Ö. Arik, Tomas Pfister:
Better Zero-Shot Reasoning with Self-Adaptive Prompting. ACL (Findings) 2023: 3493-3514 - [c28]Rui Wang, Yihe Dong, Sercan Ö. Arik, Rose Yu:
Koopman Neural Operator Forecaster for Time-series with Temporal Distributional Shifts. ICLR 2023 - [c27]Chun-Hao Chang
, Jinsung Yoon, Sercan Ö. Arik, Madeleine Udell, Tomas Pfister:
Data-Efficient and Interpretable Tabular Anomaly Detection. KDD 2023: 190-201 - [i43]Ruoxi Sun, Chun-Liang Li, Sercan Ö. Arik, Michael W. Dusenberry, Chen-Yu Lee, Tomas Pfister:
Neural Spline Search for Quantile Probabilistic Modeling. CoRR abs/2301.04857 (2023) - [i42]Si-An Chen, Chun-Liang Li, Nate Yoder, Sercan Ö. Arik, Tomas Pfister:
TSMixer: An all-MLP Architecture for Time Series Forecasting. CoRR abs/2303.06053 (2023) - [i41]Yihe Dong, Sercan Ö. Arik:
SLM: End-to-end Feature Selection via Sparse Learnable Masks. CoRR abs/2304.03202 (2023) - [i40]Jiefeng Chen, Jinsung Yoon, Sayna Ebrahimi, Sercan Ö. Arik, Somesh Jha, Tomas Pfister:
ASPEST: Bridging the Gap Between Active Learning and Selective Prediction. CoRR abs/2304.03870 (2023) - [i39]Xingchen Wan, Ruoxi Sun, Hanjun Dai, Sercan Ö. Arik, Tomas Pfister:
Better Zero-Shot Reasoning with Self-Adaptive Prompting. CoRR abs/2305.14106 (2023) - [i38]Xingchen Wan, Ruoxi Sun, Hootan Nakhost, Hanjun Dai, Julian Martin Eisenschlos, Sercan Ö. Arik, Tomas Pfister:
Universal Self-adaptive Prompting. CoRR abs/2305.14926 (2023) - [i37]Sayna Ebrahimi, Sercan Ö. Arik, Yihe Dong, Tomas Pfister:
LANISTR: Multimodal Learning from Structured and Unstructured Data. CoRR abs/2305.16556 (2023) - [i36]Ruoxi Sun, Sercan Ö. Arik, Hootan Nakhost, Hanjun Dai, Rajarishi Sinha, Pengcheng Yin, Tomas Pfister:
SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL. CoRR abs/2306.00739 (2023) - [i35]Nicasia Beebe-Wang, Sayna Ebrahimi, Jinsung Yoon, Sercan Ö. Arik, Tomas Pfister:
PAITS: Pretraining and Augmentation for Irregularly-Sampled Time Series. CoRR abs/2308.13703 (2023) - 2022
- [j8]Thomas C. Tsai, Sercan Ö. Arik, Benjamin H. Jacobson, Jinsung Yoon, Nate Yoder, Dario Sava, Margaret Mitchell, Garth Graham, Tomas Pfister:
Algorithmic fairness in pandemic forecasting: lessons from COVID-19. npj Digit. Medicine 5 (2022) - [j7]Jinsung Yoon, Sercan Ö. Arik, Tomas Pfister:
LIMIS: Locally Interpretable Modeling using Instance-wise Subsampling. Trans. Mach. Learn. Res. 2022 (2022) - [j6]Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li, Sercan Ö. Arik, Chen-Yu Lee, Tomas Pfister:
Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection. Trans. Mach. Learn. Res. 2022 (2022) - [c26]Zizhao Zhang, Han Zhang, Long Zhao, Ting Chen, Sercan Ö. Arik, Tomas Pfister:
Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual Understanding. AAAI 2022: 3417-3425 - [c25]Sana Tonekaboni, Chun-Liang Li, Sercan Ö. Arik, Anna Goldenberg, Tomas Pfister:
Decoupling Local and Global Representations of Time Series. AISTATS 2022: 8700-8714 - [c24]Serdar Ozsoy, Shadi Hamdan, Sercan Ö. Arik, Deniz Yuret, Alper T. Erdogan:
Self-Supervised Learning with an Information Maximization Criterion. NeurIPS 2022 - [i34]Sana Tonekaboni, Chun-Liang Li, Sercan Ö. Arik, Anna Goldenberg, Tomas Pfister:
Decoupling Local and Global Representations of Time Series. CoRR abs/2202.02262 (2022) - [i33]Sercan Ö. Arik, Nathanael C. Yoder, Tomas Pfister:
Self-Adaptive Forecasting for Improved Deep Learning on Non-Stationary Time-Series. CoRR abs/2202.02403 (2022) - [i32]Chun-Hao Chang, Jinsung Yoon, Sercan Ö. Arik, Madeleine Udell, Tomas Pfister:
Data-Efficient and Interpretable Tabular Anomaly Detection. CoRR abs/2203.02034 (2022) - [i31]Aya Abdelsalam Ismail, Sercan Ö. Arik, Jinsung Yoon, Ankur Taly, Soheil Feizi, Tomas Pfister:
Interpretable Mixture of Experts for Structured Data. CoRR abs/2206.02107 (2022) - [i30]Yunhao Ge, Sercan Ö. Arik, Jinsung Yoon, Ao Xu, Laurent Itti, Tomas Pfister:
Invariant Structure Learning for Better Generalization and Causal Explainability. CoRR abs/2206.06469 (2022) - [i29]Sayna Ebrahimi, Sercan Ö. Arik, Tomas Pfister:
Test-Time Adaptation for Visual Document Understanding. CoRR abs/2206.07240 (2022) - [i28]Serdar Ozsoy, Shadi Hamdan, Sercan Ö. Arik, Deniz Yuret, Alper T. Erdogan:
Self-Supervised Learning with an Information Maximization Criterion. CoRR abs/2209.07999 (2022) - [i27]Rui Wang, Yihe Dong, Sercan Ö. Arik, Rose Yu:
Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts. CoRR abs/2210.03675 (2022) - [i26]Zachary Izzo, Jinsung Yoon, Sercan Ö. Arik, James Zou:
Provable Membership Inference Privacy. CoRR abs/2211.06582 (2022) - [i25]Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li, Sercan Ö. Arik, Tomas Pfister:
SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch. CoRR abs/2212.00173 (2022) - 2021
- [j5]Sercan Ö. Arik, Joel Shor, Rajarishi Sinha, Jinsung Yoon, Joseph R. Ledsam, Long T. Le, Michael W. Dusenberry, Nathanael C. Yoder, Kris Popendorf, Arkady Epshteyn, Johan Euphrosine, Elli Kanal, Isaac Jones, Chun-Liang Li, Beth Luan, Joe Mckenna, Vikas Menon, Shashank Singh, Mimi Sun, Ashwin Sura Ravi, Leyou Zhang, Dario Sava, Kane Cunningham, Hiroki Kayama, Thomas C. Tsai, Daisuke Yoneoka, Shuhei Nomura, Hiroaki Miyata, Tomas Pfister:
A prospective evaluation of AI-augmented epidemiology to forecast COVID-19 in the USA and Japan. npj Digit. Medicine 4 (2021) - [c23]Sercan Ö. Arik, Tomas Pfister:
TabNet: Attentive Interpretable Tabular Learning. AAAI 2021: 6679-6687 - [c22]Sungyong Seo, Sercan Ö. Arik, Jinsung Yoon, Xiang Zhang, Kihyuk Sohn, Tomas Pfister:
Controlling Neural Networks with Rule Representations. NeurIPS 2021: 11196-11207 - [i24]Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li, Sercan Ö. Arik, Chen-Yu Lee, Tomas Pfister:
Self-Trained One-class Classification for Unsupervised Anomaly Detection. CoRR abs/2106.06115 (2021) - [i23]Sungyong Seo, Sercan Ö. Arik, Jinsung Yoon, Xiang Zhang, Kihyuk Sohn, Tomas Pfister:
Controlling Neural Networks with Rule Representations. CoRR abs/2106.07804 (2021) - 2020
- [j4]Sercan Ömer Arik, Tomas Pfister:
ProtoAttend: Attention-Based Prototypical Learning. J. Mach. Learn. Res. 21: 210:1-210:35 (2020) - [c21]Zizhao Zhang, Han Zhang, Sercan Ömer Arik, Honglak Lee, Tomas Pfister:
Distilling Effective Supervision From Severe Label Noise. CVPR 2020: 9291-9300 - [c20]Linchao Zhu, Sercan Ömer Arik, Yi Yang, Tomas Pfister:
Learning to Transfer Learn: Reinforcement Learning-Based Selection for Adaptive Transfer Learning. ECCV (27) 2020: 342-358 - [c19]Mingfei Gao, Zizhao Zhang, Guo Yu, Sercan Ömer Arik, Larry S. Davis, Tomas Pfister:
Consistency-Based Semi-supervised Active Learning: Towards Minimizing Labeling Cost. ECCV (10) 2020: 510-526 - [c18]Chen Xing, Sercan Ömer Arik, Zizhao Zhang, Tomas Pfister:
Distance-Based Learning from Errors for Confidence Calibration. ICLR 2020 - [c17]Jinsung Yoon, Sercan Ömer Arik, Tomas Pfister:
Data Valuation using Reinforcement Learning. ICML 2020: 10842-10851 - [c16]Sercan Ömer Arik, Chun-Liang Li, Jinsung Yoon, Rajarishi Sinha, Arkady Epshteyn, Long T. Le, Vikas Menon, Shashank Singh, Leyou Zhang, Martin Nikoltchev, Yash Sonthalia, Hootan Nakhost, Elli Kanal, Tomas Pfister:
Interpretable Sequence Learning for Covid-19 Forecasting. NeurIPS 2020 - [c15]Chih-Kuan Yeh, Been Kim, Sercan Ömer Arik, Chun-Liang Li, Tomas Pfister, Pradeep Ravikumar:
On Completeness-aware Concept-Based Explanations in Deep Neural Networks. NeurIPS 2020 - [i22]Yu-Han Liu, Sercan Ö. Arik:
Explaining Deep Neural Networks using Unsupervised Clustering. CoRR abs/2007.07477 (2020) - [i21]Sercan Ömer Arik, Chun-Liang Li, Jinsung Yoon, Rajarishi Sinha, Arkady Epshteyn, Long T. Le, Vikas Menon, Shashank Singh, Leyou Zhang, Nate Yoder, Martin Nikoltchev, Yash Sonthalia, Hootan Nakhost, Elli Kanal, Tomas Pfister:
Interpretable Sequence Learning for COVID-19 Forecasting. CoRR abs/2008.00646 (2020)
2010 – 2019
- 2019
- [j3]Sercan Ömer Arik, Heewoo Jun, Gregory Frederick Diamos:
Fast Spectrogram Inversion Using Multi-Head Convolutional Neural Networks. IEEE Signal Process. Lett. 26(1): 94-98 (2019) - [i20]Sercan Ömer Arik, Tomas Pfister:
Attention-Based Prototypical Learning Towards Interpretable, Confident and Robust Deep Neural Networks. CoRR abs/1902.06292 (2019) - [i19]Yanqi Zhou, Peng Wang, Sercan Ömer Arik, Haonan Yu, Syed Zawad, Feng Yan, Greg Diamos:
EPNAS: Efficient Progressive Neural Architecture Search. CoRR abs/1907.04648 (2019) - [i18]Sercan Ömer Arik, Tomas Pfister:
TabNet: Attentive Interpretable Tabular Learning. CoRR abs/1908.07442 (2019) - [i17]Linchao Zhu, Sercan Ömer Arik, Yi Yang, Tomas Pfister:
Learning to Transfer Learn. CoRR abs/1908.11406 (2019) - [i16]Jinsung Yoon, Sercan Ömer Arik, Tomas Pfister:
Data Valuation using Reinforcement Learning. CoRR abs/1909.11671 (2019) - [i15]Jinsung Yoon, Sercan Ömer Arik, Tomas Pfister:
RL-LIM: Reinforcement Learning-based Locally Interpretable Modeling. CoRR abs/1909.12367 (2019) - [i14]Zizhao Zhang, Han Zhang, Sercan Ömer Arik, Honglak Lee, Tomas Pfister:
IEG: Robust Neural Network Training to Tackle Severe Label Noise. CoRR abs/1910.00701 (2019) - [i13]Mingfei Gao, Zizhao Zhang, Guo Yu, Sercan Ömer Arik, Larry S. Davis, Tomas Pfister:
Consistency-Based Semi-Supervised Active Learning: Towards Minimizing Labeling Cost. CoRR abs/1910.07153 (2019) - [i12]Chih-Kuan Yeh, Been Kim, Sercan Ömer Arik, Chun-Liang Li, Pradeep Ravikumar, Tomas Pfister:
On Concept-Based Explanations in Deep Neural Networks. CoRR abs/1910.07969 (2019) - [i11]Chen Xing, Sercan Ömer Arik, Zizhao Zhang, Tomas Pfister:
Distance-Based Learning from Errors for Confidence Calibration. CoRR abs/1912.01730 (2019) - [i10]Bryan Lim, Sercan Ömer Arik, Nicolas Loeff, Tomas Pfister:
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting. CoRR abs/1912.09363 (2019) - 2018
- [j2]Alaelson C. Jatoba-Neto, Darli A. A. Mello, Christian Esteve Rothenberg, Sercan Ö. Arik, Joseph M. Kahn:
Scaling SDM Optical Networks Using Full-Spectrum Spatial Switching. JOCN 10(12): 991-1004 (2018) - [c14]Wei Ping, Kainan Peng, Andrew Gibiansky, Sercan Ömer Arik, Ajay Kannan, Sharan Narang, Jonathan Raiman, John Miller:
Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning. ICLR (Poster) 2018 - [c13]Sercan Ömer Arik, Jitong Chen, Kainan Peng, Wei Ping, Yanqi Zhou:
Neural Voice Cloning with a Few Samples. NeurIPS 2018: 10040-10050 - [i9]Sercan Ömer Arik, Jitong Chen, Kainan Peng, Wei Ping, Yanqi Zhou:
Neural Voice Cloning with a Few Samples. CoRR abs/1802.06006 (2018) - [i8]Yanqi Zhou, Siavash Ebrahimi, Sercan Ömer Arik, Haonan Yu, Hairong Liu, Greg Diamos:
Resource-Efficient Neural Architect. CoRR abs/1806.07912 (2018) - [i7]Sercan Ömer Arik, Heewoo Jun, Gregory F. Diamos:
Fast Spectrogram Inversion using Multi-head Convolutional Neural Networks. CoRR abs/1808.06719 (2018) - 2017
- [c12]Alaelson C. Jatoba-Neto, Christian Esteve Rothenberg, Darli A. A. Mello, Sercan Ömer Arik, Joseph M. Kahn:
Scaling optical networks using full-spectrum spatial switching. HPSR 2017: 1-6 - [c11]Sercan Ömer Arik, Mike Chrzanowski, Adam Coates, Gregory Frederick Diamos, Andrew Gibiansky, Yongguo Kang, Xian Li, John Miller, Andrew Y. Ng, Jonathan Raiman, Shubho Sengupta, Mohammad Shoeybi:
Deep Voice: Real-time Neural Text-to-Speech. ICML 2017: 195-204 - [c10]Karthik Choutagunta, Sercan Ö. Arik, Mehrad Moradshahi, Joseph M. Kahn:
Optical MIMO signal processing for direct-detection mode-division multiplexing. ICTON 2017: 1 - [c9]Sercan Ömer Arik, Markus Kliegl, Rewon Child, Joel Hestness, Andrew Gibiansky, Christopher Fougner, Ryan Prenger, Adam Coates:
Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spotting. INTERSPEECH 2017: 1606-1610 - [c8]Andrew Gibiansky, Sercan Ömer Arik, Gregory Frederick Diamos, John Miller, Kainan Peng, Wei Ping, Jonathan Raiman, Yanqi Zhou:
Deep Voice 2: Multi-Speaker Neural Text-to-Speech. NIPS 2017: 2962-2970 - [c7]Omar D. Domingues, Darli A. A. Mello, Reginaldo Silva, Sercan Ömer Arik, Joseph M. Kahn:
Capacity limits of space-division multiplexed submarine links subject to nonlinearities and power feed constraints. OFC 2017: 1-3 - [i6]Sercan Ömer Arik, Mike Chrzanowski, Adam Coates, Greg Diamos, Andrew Gibiansky, Yongguo Kang, Xian Li, John Miller, Jonathan Raiman, Shubho Sengupta, Mohammad Shoeybi:
Deep Voice: Real-time Neural Text-to-Speech. CoRR abs/1702.07825 (2017) - [i5]Sercan Ömer Arik, Markus Kliegl, Rewon Child, Joel Hestness, Andrew Gibiansky, Christopher Fougner, Ryan Prenger, Adam Coates:
Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spotting. CoRR abs/1703.05390 (2017) - [i4]Sercan Ömer Arik, Gregory F. Diamos, Andrew Gibiansky, John Miller, Kainan Peng, Wei Ping, Jonathan Raiman, Yanqi Zhou:
Deep Voice 2: Multi-Speaker Neural Text-to-Speech. CoRR abs/1705.08947 (2017) - [i3]Sercan Ömer Arik, Joseph M. Kahn:
Low-complexity implementation of convex optimization-based phase retrieval. CoRR abs/1707.05797 (2017) - [i2]Wei Ping, Kainan Peng, Andrew Gibiansky, Sercan Ömer Arik, Ajay Kannan, Sharan Narang, Jonathan Raiman, John Miller:
Deep Voice 3: 2000-Speaker Neural Text-to-Speech. CoRR abs/1710.07654 (2017) - 2015
- [c6]Sercan Ömer Arik, Keang-Po Ho, Joseph M. Kahn:
Group delay statistics and management in mode-division multiplexing. ACSSC 2015: 991-998 - [c5]Sercan Ömer Arik, Daulet Askarov, Joseph M. Kahn:
MIMO DSP complexity in mode-division multiplexing. OFC 2015: 1-3 - [c4]Joseph M. Kahn, Sercan Ömer Arik, Keang-Po Ho:
MIMO channel statistics and signal processing in mode-division multiplexing systems. SPAWC 2015: 440-444 - 2014
- [j1]Sercan Ömer Arik, Joseph M. Kahn, Keang-Po Ho:
MIMO Signal Processing for Mode-Division Multiplexing: An overview of channel models and signal processing architectures. IEEE Signal Process. Mag. 31(2): 25-34 (2014) - [c3]Sercan Ö. Arik, David S. Millar, Toshiaki Koike-Akino, Keisuke Kojima, Kieran Parsons:
High-dimensional modulation for mode-division multiplexing. OFC 2014: 1-3 - [c2]David S. Millar, Toshiaki Koike-Akino, Sercan Ö. Arik, Keisuke Kojima, Kieran Parsons:
Comparison of quaternary block-coding and sphere-cutting for high-dimensional modulation. OFC 2014: 1-3 - [i1]Sercan Ö. Arik, Sukru Burc Eryilmaz, Adam Goldberg:
Supervised classification-based stock prediction and portfolio optimization. CoRR abs/1406.0824 (2014) - 2011
- [c1]Sercan Ömer Arik, Elif Vural, Pascal Frossard:
Alignment of uncalibrated images for multi-view classification. ICIP 2011: 2365-2368
Coauthor Index
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