Nathanael C. Yoder

According to our database, Nathanael C. Yoder authored at least 6 papers between 2020 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

TSMixer: An all-MLP Architecture for Time Series Forecasting.

Si-An Chen, Chun-Liang Li, Nate Yoder, Sercan Ö. Arik, Tomas Pfister

CoRR, 2023

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

Self-Adaptive Forecasting for Improved Deep Learning on Non-Stationary Time-Series.

Sercan Ö. Arik, Nathanael C. Yoder, Tomas Pfister

CoRR, 2022

2021

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, 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

npj Digit. Medicine, 2021

2020

FilterNet: A Many-to-Many Deep Learning Architecture for Time Series Classification.

Robert D. Chambers, Nathanael C. Yoder

Sensors, 2020

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, Leyou Zhang, Nate Yoder, Martin Nikoltchev, Yash Sonthalia, Hootan Nakhost, Elli Kanal, Tomas Pfister

CoRR, 2020