no code implementations • NeurIPS 2023 • David Lüdke, Marin Biloš, Oleksandr Shchur, Marten Lienen, Stephan Günnemann
Autoregressive neural networks within the temporal point process (TPP) framework have become the standard for modeling continuous-time event data.
1 code implementation • 12 Oct 2023 • Kashif Rasul, Arjun Ashok, Andrew Robert Williams, Hena Ghonia, Rishika Bhagwatkar, Arian Khorasani, Mohammad Javad Darvishi Bayazi, George Adamopoulos, Roland Riachi, Nadhir Hassen, Marin Biloš, Sahil Garg, Anderson Schneider, Nicolas Chapados, Alexandre Drouin, Valentina Zantedeschi, Yuriy Nevmyvaka, Irina Rish
Over the past years, foundation models have caused a paradigm shift in machine learning due to their unprecedented capabilities for zero-shot and few-shot generalization.
no code implementations • 4 Nov 2022 • Marin Biloš, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka, Stephan Günnemann
Temporal data such as time series can be viewed as discretized measurements of the underlying function.
no code implementations • 19 Oct 2022 • Marin Biloš, Emanuel Ramneantu, Stephan Günnemann
Observations made in continuous time are often irregular and contain the missing values across different channels.
1 code implementation • NeurIPS 2021 • Marin Biloš, Johanna Sommer, Syama Sundar Rangapuram, Tim Januschowski, Stephan Günnemann
Neural ordinary differential equations describe how values change in time.
Ranked #3 on Multivariate Time Series Forecasting on MIMIC-III
no code implementations • 7 Oct 2020 • Marin Biloš, Stephan Günnemann
Modeling sets is an important problem in machine learning since this type of data can be found in many domains.
no code implementations • 28 Sep 2020 • Marin Biloš, Stephan Günnemann
To model this behavior, it is enough to transform the samples from the uniform process with a sufficiently complex equivariant function.
no code implementations • 15 Jul 2020 • Nick Harmening, Marin Biloš, Stephan Günnemann
Determining the traffic scenario space is a major challenge for the homologation and coverage assessment of automated driving functions.
1 code implementation • NeurIPS 2020 • Oleksandr Shchur, Nicholas Gao, Marin Biloš, Stephan Günnemann
Temporal point process (TPP) models combined with recurrent neural networks provide a powerful framework for modeling continuous-time event data.
1 code implementation • NeurIPS 2019 • Marin Biloš, Bertrand Charpentier, Stephan Günnemann
Asynchronous event sequences are the basis of many applications throughout different industries.
3 code implementations • ICLR 2020 • Oleksandr Shchur, Marin Biloš, Stephan Günnemann
The standard way of learning in such models is by estimating the conditional intensity function.