2 citations · 6 across the 10 of their papers we have counts for
10 papers
Physiome-ODE: A Benchmark for Irregularly Sampled Multivariate Time Series Forecasting Based on Biological ODEs
Christian Klötergens, Vijaya Krishna Yalavarthi, Randolf Scholz +3
State-of-the-art methods for forecasting irregularly sampled time series with missing values predominantly rely on just four datasets and a few small toy examples for evaluation. W…
Marginalization Consistent Probabilistic Forecasting of Irregular Time Series via Mixture of Separable flows
Vijaya Krishna Yalavarthi, Randolf Scholz, Christian Kloetergens +3
Probabilistic forecasting models for joint distributions of targets in irregular time series with missing values are a heavily under-researched area in machine learning, with, to t…
Probabilistic Forecasting of Irregular Time Series via Conditional Flows
Vijaya Krishna Yalavarthi, Randolf Scholz, Stefan Born +1
Probabilistic forecasting of irregularly sampled multivariate time series with missing values is an important problem in many fields, including health care, astronomy, and climate.…
Analytical solution for the long- and short-range every-pair-interactions model
Fabiano L. Ribeiro, Yunfei Li, Stefan Born +1
Many physical, biological, and social systems exhibit emergent properties that arise from the interactions between their components (cells). In this study, we systematically treat…
Forecasting Irregularly Sampled Time Series using Graphs
Vijaya Krishna Yalavarthi, Kiran Madhusudhanan, Randolf Sholz +5
Forecasting irregularly sampled time series with missing values is a crucial task for numerous real-world applications such as healthcare, astronomy, and climate sciences. State-of…
Temporal Saliency Detection Towards Explainable Transformer-based Timeseries Forecasting
Nghia Duong-Trung, Duc-Manh Nguyen, Danh Le-Phuoc
Despite the notable advancements in numerous Transformer-based models, the task of long multi-horizon time series forecasting remains a persistent challenge, especially towards exp…
When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development
Nghia Duong-Trung, Stefan Born, Jong Woo Kim +9
Machine learning (ML) is becoming increasingly crucial in many fields of engineering but has not yet played out its full potential in bioprocess engineering. While experimentation…
Yformer: U-Net Inspired Transformer Architecture for Far Horizon Time Series Forecasting
Kiran Madhusudhanan, Johannes Burchert, Nghia Duong-Trung +2
Time series data is ubiquitous in research as well as in a wide variety of industrial applications. Effectively analyzing the available historical data and providing insights into…
Quasiconformal distortion of projective transformations and discrete conformal maps
Stefan Born, Ulrike Bücking, Boris Springborn
We consider the quasiconformal dilatation of projective transformations of the real projective plane. For non-affine transformations, the contour lines of dilatation form a hyperbo…
Boundedness of Functions on Product Spaces by Sums of Functions on the Factors
Stefan Born, Alexander Dirmeier
We investigate sufficient conditions for real-valued functions on product spaces to be bounded from above by sums or products of functions which depend only on points in the respec…