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Marin Biloš
Marin Biloš
Verified email at in.tum.de - Homepage
Title
Cited by
Cited by
Year
Intensity-Free Learning of Temporal Point Processes
O Shchur, M Biloš, S Günnemann
International Conference on Learning Representations, 2020
1492020
Neural flows: Efficient alternative to neural ODEs
M Biloš, J Sommer, SS Rangapuram, T Januschowski, S Günnemann
Advances in neural information processing systems 34, 21325-21337, 2021
482021
Uncertainty on asynchronous time event prediction
M Biloš, B Charpentier, S Günnemann
Neural Information Processing Systems, 2019
332019
Fast and flexible temporal point processes with triangular maps
O Shchur, N Gao, M Biloš, S Günnemann
Neural Information Processing Systems, 2020
262020
Modeling temporal data as continuous functions with stochastic process diffusion
M Biloš, K Rasul, A Schneider, Y Nevmyvaka, S Günnemann
International Conference on Machine Learning, 2452-2470, 2023
23*2023
Lag-llama: Towards foundation models for time series forecasting
K Rasul, A Ashok, AR Williams, A Khorasani, G Adamopoulos, ...
arXiv preprint arXiv:2310.08278, 2023
182023
Scalable Normalizing Flows for Permutation Invariant Densities
M Biloš, S Günnemann
International Conference on Machine Learning, 2021
18*2021
Deep representation learning and clustering of traffic scenarios
N Harmening, M Biloš, S Günnemann
arXiv preprint arXiv:2007.07740, 2020
172020
Towards linking social media profiles with user’s WiFi preferred network list
A Dagelić, M Čagalj, T Perković, M Biloš
Ad Hoc Networks 107, 102244, 2020
32020
Add and thin: Diffusion for temporal point processes
D Lüdke, M Biloš, O Shchur, M Lienen, S Günnemann
Advances in Neural Information Processing Systems 36, 2024
22024
Irregularly-sampled time series modeling with spline networks
M Biloš, E Ramneantu, S Günnemann
arXiv preprint arXiv:2210.10630, 2022
22022
Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
K Rasul, A Ashok, AR Williams, H Ghonia, R Bhagwatkar, A Khorasani, ...
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