| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 1611 | 2023 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 412 | 2024 |
| Deep Learning for Time Series Forecasting: Tutorial and Literature Survey K Benidis, SS Rangapuram, V Flunkert, Y Wang, D Maddix, C Turkmen, ... ACM Computing Surveys (CSUR), 2020 | 284* | 2020 |
| All eyes on you: Distributed Multi-Dimensional IoT microservice anomaly detection MO Pahl, FX Aubet 2018 14th International Conference on Network and Service Management (CNSM …, 2018 | 179 | 2018 |
| Neural Contextual Anomaly Detection for Time Series CU Carmona*, FX Aubet*, V Flunkert, J Gasthaus IJCAI 2022, 2021 | 74 | 2021 |
| Graph-based IoT microservice security MO Pahl, FX Aubet, S Liebald NOMS 2018-2018 IEEE/IFIP Network Operations and Management Symposium, 1-3, 2018 | 48 | 2018 |
| DS2OS traffic traces FX Aubet, MO Pahl | 42* | 2018 |
| Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting Y Park, D Maddix, FX Aubet, K Kan, J Gasthaus, Y Wang AISTATS 2022, 2021 | 34 | 2021 |
| Multivariate Quantile Function Forecaster K Kan, FX Aubet, T Januschowski, Y Park, K Benidis, L Ruthotto, ... AISTATS 2022, 2022 | 26 | 2022 |
| Deep learning-aided resource orchestration for vehicular safety communication MI Khan, FX Aubet, MO Pahl, J Härri 2019 Wireless Days (WD), 1-8, 2019 | 23* | 2019 |
| A study of joint graph inference and forecasting D Zügner, FX Aubet, VG Satorras, T Januschowski, S Günnemann, ... ICML 2021 Time Series Workshop, 2021 | 15 | 2021 |
| Diverse counterfactual explanations for anomaly detection in time series D Sulem, M Donini, MB Zafar, FX Aubet, J Gasthaus, T Januschowski, ... arXiv preprint arXiv:2203.11103, 2022 | 11 | 2022 |
| Machine learning-based adaptive anomaly detection in smart spaces FX Aubet Thesis, 2018 | 10 | 2018 |
| Online time series anomaly detection with state space gaussian processes C Bock, FX Aubet, J Gasthaus, A Kan, M Chen, L Callot arXiv preprint arXiv:2201.06763, 2022 | 8 | 2022 |
| Monte Carlo EM for Deep Time Series Anomaly Detection FX Aubet, D Zügner, J Gasthaus ICML 2021 Time Series Workshop, 2021 | 7 | 2021 |
| Spliced Binned-Pareto Distribution for Robust Modeling of Heavy-tailed Time Series E Ehrlich, L Callot, FX Aubet ICLR 2021 RobustML Workshop, 2021 | 7 | 2021 |
| Ds2Os Traffic Traces IoT Traffic Traces Gathered in a The Ds2Os IoT Environment MO Pahl, FX Aubet Int J Info Sec (IJIS), 2018 | 7 | 2018 |
| Graph-based anomaly detection for iot microservices FX Aubet, MO Pahl, S Liebald, MR Norouzian Measurements 120 (140), 160, 2018 | 7 | 2018 |
| Ds2os traffic traces iot traffic traces gathered in a the ds2os iot environment, 2018 MO Pahl, FX Aubet URL: https://www. kaggle. com/francoisxa/ds2ostraffictraces, 0 | 5 | |
| Obeying the Order: Introducing Ordered Transfer Hyperparameter Optimisation S Passano Hellan, H Shen, FX Aubet, D Salinas, A Klein arXiv e-prints, arXiv: 2306.16916, 2023 | 3* | 2023 |
Jan GasthausAWS AI Labs, Amazon Web ServicesVerified email at ucl.ac.uk
