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2020 – today
- 2024
[i4]Yihe Dong, Sercan Ö. Arik, Nathanael C. Yoder, Tomas Pfister:
Learned Feature Importance Scores for Automated Feature Engineering. CoRR abs/2406.04153 (2024)- 2023
- [j4]Si-An Chen, Chun-Liang Li, Sercan Ö. Arik, Nathanael C. Yoder, Tomas Pfister:
TSMixer: An All-MLP Architecture for Time Series Forecast-ing. Trans. Mach. Learn. Res. 2023 (2023) - [i3]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) - 2022
- [j3]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) - [i2]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) - 2021
- [j2]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) - 2020
- [j1]Robert D. Chambers, Nathanael C. Yoder:
FilterNet: A Many-to-Many Deep Learning Architecture for Time Series Classification. Sensors 20(9): 2498 (2020) - [i1]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)
Coauthor Index
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