Quasiconformal distortion of projective transformations and discrete conformal maps. - Stefan Born; Ulrike Bücking; Boris Springborn (CoRR, 2015)

Quasiconformal Dilatation of Projective Transformations and Discrete Conformal Maps. - Stefan Born; Ulrike Bücking; Boris Springborn (Discret. Comput. Geom., 2017)

Yformer: U-Net Inspired Transformer Architecture for Far Horizon Time Series Forecasting. - Kiran Madhusudhanan; Johannes Burchert; Nghia Duong-Trung; Stefan Born; Lars Schmidt-Thieme (CoRR, 2021)

Fitting nonlinear models to continuous oxygen data with oscillatory signal variations via a loss based on DynamicTime Warping. - Judit Aizpuru; Annina Karolin Kemmer; Jong Woo Kim; Stefan Born; Peter Neubauer; Mariano Nicolás Cruz Bournazou; Tilman Barz (CoRR, 2021)

U-Net Inspired Transformer Architecture for Far Horizon Time Series Forecasting. - Kiran Madhusudhanan; Johannes Burchert; Nghia Duong-Trung; Stefan Born; Lars Schmidt-Thieme (ECML/PKDD (6), 2022)

When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development. - Nghia Duong-Trung; Stefan Born; Jong Woo Kim; Marie-Therese Schermeyer; Katharina Paulick; Maxim Borisyak; Ernesto C. Martínez; Mariano Nicolás Cruz Bournazou; Thorben Werner; Randolf Scholz; Lars Schmidt-Thieme; Peter Neubauer (CoRR, 2022)

Put Attention to Temporal Saliency Patterns of Multi-Horizon Time Series. - Nghia Duong-Trung; Stefan Born; Kiran Madhusudhanan; Randolf Scholz; Johannes Burchert; Danh Le Phuoc; Lars Schmidt-Thieme (CoRR, 2022)

Forecasting Irregularly Sampled Time Series using Graphs. - Vijaya Krishna Yalavarthi; Kiran Madusudanan; Randolf Scholz; Nourhan Ahmed; Johannes Burchert; Shayan Jawed; Stefan Born; Lars Schmidt-Thieme (CoRR, 2023)

Deep Set Neural Networks for forecasting asynchronous bioprocess timeseries. - Maxim Borisyak; Stefan Born; Peter Neubauer; Mariano Nicolás Cruz Bournazou (CoRR, 2023)

Deep Learning for Fast Inference of Mechanistic Models' Parameters. - Maxim Borisyak; Stefan Born; Peter Neubauer; Mariano Nicolás Cruz Bournazou (CoRR, 2023)

GraFITi: Graphs for Forecasting Irregularly Sampled Time Series. - Vijaya Krishna Yalavarthi; Kiran Madhusudhanan; Randolf Scholz; Nourhan Ahmed; Johannes Burchert; Shayan Jawed; Stefan Born; Lars Schmidt-Thieme (AAAI, 2024)

LARA - embracing almost fully automated experimentation from ground up by using semantic web technologies in Life Sciences. - Mark Doerr; Stefan Born (SWAT4HCLS, 2024)

Probabilistic Forecasting of Irregular Time Series via Conditional Flows. - Vijaya Krishna Yalavarthi; Randolf Scholz; Stefan Born; Lars Schmidt-Thieme (CoRR, 2024)

Marginalization Consistent Mixture of Separable Flows for Probabilistic Irregular Time Series Forecasting. - Vijaya Krishna Yalavarthi; Randolf Scholz; Kiran Madhusudhanan; Stefan Born; Lars Schmidt-Thieme (CoRR, 2024)

Probabilistic Forecasting of Irregularly Sampled Time Series with Missing Values via Conditional Normalizing Flows. - Vijaya Krishna Yalavarthi; Randolf Scholz; Stefan Born; Lars Schmidt-Thieme (AAAI, 2025)

Physiome-ODE: A Benchmark for Irregularly Sampled Multivariate Time-Series Forecasting Based on Biological ODEs. - Christian Klötergens; Vijaya Krishna Yalavarthi; Randolf Scholz; Maximilian Stubbemann; Stefan Born; Lars Schmidt-Thieme (ICLR, 2025)