Interpretable Sequence Learning for COVID-19 Forecasting. - Sercan Ömer Arik; Chun-Liang Li; Jinsung Yoon; Rajarishi Sinha; Arkady Epshteyn; Long T. Le; Vikas Menon; Shashank Singh 005; Leyou Zhang; Nate Yoder; Martin Nikoltchev; Yash Sonthalia; et al. (CoRR, 2020)

FilterNet: A Many-to-Many Deep Learning Architecture for Time Series Classification. - Robert D. Chambers; Nathanael C. Yoder (Sensors, 2020)

A prospective evaluation of AI-augmented epidemiology to forecast COVID-19 in the USA and Japan. - 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; et al. (npj Digit. Medicine, 2021)

Self-Adaptive Forecasting for Improved Deep Learning on Non-Stationary Time-Series. - Sercan Ö. Arik; Nathanael C. Yoder; Tomas Pfister (CoRR, 2022)

Algorithmic fairness in pandemic forecasting: lessons from COVID-19. - Thomas C. Tsai; Sercan Ö. Arik; Benjamin H. Jacobson; Jinsung Yoon; Nate Yoder; Dario Sava; Margaret Mitchell; Garth Graham; Tomas Pfister (npj Digit. Medicine, 2022)

TSMixer: An all-MLP Architecture for Time Series Forecasting. - Si-An Chen; Chun-Liang Li; Nate Yoder; Sercan Ö. Arik; Tomas Pfister (CoRR, 2023)

TSMixer: An All-MLP Architecture for Time Series Forecast-ing. - Si-An Chen; Chun-Liang Li; Sercan Ö. Arik; Nathanael C. Yoder; Tomas Pfister (Trans. Mach. Learn. Res., 2023)

Learned Feature Importance Scores for Automated Feature Engineering. - Yihe Dong; Sercan Ö. Arik; Nathanael C. Yoder; Tomas Pfister (CoRR, 2024)