| Neural transformation learning for deep anomaly detection beyond images C Qiu, T Pfrommer, M Kloft, S Mandt, M Rudolph International conference on machine learning, 8703-8714, 2021 | 102 | 2021 |
| Latent Outlier Exposure for Anomaly Detection with Contaminated Data C Qiu, A Li, M Kloft, M Rudolph, S Mandt International Conference on Machine Learning, 18153-18167, 2022 | 33 | 2022 |
| Raising the Bar in Graph-level Anomaly Detection C Qiu, M Kloft, S Mandt, M Rudolph International Joint Conference on Artificial Intelligence, 2022 | 30 | 2022 |
| Learning topometric semantic maps from occupancy grids M Hiller, C Qiu, F Particke, C Hofmann, J Thielecke 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2019 | 27 | 2019 |
| Detecting anomalies within time series using local neural transformations T Schneider, C Qiu, M Kloft, DA Latif, S Staab, S Mandt, M Rudolph arXiv preprint arXiv:2202.03944, 2022 | 16 | 2022 |
| Zero-shot anomaly detection via batch normalization A Li, C Qiu, M Kloft, P Smyth, M Rudolph, S Mandt Advances in Neural Information Processing Systems 36, 2024 | 5* | 2024 |
| Deep anomaly detection under labeling budget constraints A Li, C Qiu, M Kloft, P Smyth, S Mandt, M Rudolph International Conference on Machine Learning, 19882-19910, 2023 | 4 | 2023 |
| Switching recurrent Kalman networks G Nguyen-Quynh, P Becker, C Qiu, M Rudolph, G Neumann arXiv preprint arXiv:2111.08291, 2021 | 4 | 2021 |
| Text-driven Prompt Generation for Vision-Language Models in Federated Learning C Qiu, X Li, CK Mummadi, MR Ganesh, Z Li, L Peng, WY Lin The Twelfth International Conference on Learning Representations, 2024 | 3 | 2024 |
| Deterministic inference of neural stochastic differential equations A Look, C Qiu, M Rudolph, J Peters, M Kandemir Preprint at arXiv https://doi. org/10.48550/arXiv, 2006 | 3 | 2006 |
| Forecasting with deep state space models C Qiu, MR Rudolph US Patent App. 17/407,621, 2022 | 2 | 2022 |
| History marginalization improves forecasting in variational recurrent neural networks C Qiu, S Mandt, M Rudolph Entropy 23 (12), 1563, 2021 | 2* | 2021 |
| Switching recurrent kalman network G Nguyen, C Qiu, P Becker, M Rudolph, G Neumann US Patent App. 17/516,330, 2023 | 1 | 2023 |
| Anomalous region detection with local neural transformations M Rudolph, C Qiu, T Schneider US Patent App. 17/372,204, 2023 | 1 | 2023 |
| Self-Supervised Anomaly Detection with Neural Transformations C Qiu Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau, 2023 | 1 | 2023 |
| Machine learned anomaly detection C Qiu, MR Rudolph, T Pfrommer US Patent App. 17/651,917, 2022 | 1 | 2022 |
| Method and system for graph level anomaly detection C Qiu, M Rudolph US Patent App. 17/734,067, 2023 | | 2023 |
| Model Selection of Anomaly Detectors in the Absence of Labeled Validation Data C Fung, C Qiu, A Li, M Rudolph arXiv preprint arXiv:2310.10461, 2023 | | 2023 |
| Latent outlier exposure for anomaly detection M Rudolph, C Qiu US Patent App. 17/670,071, 2023 | | 2023 |
| Data driven recognition of anomalies and continuation of sensor data T Bu, C Qiu, MR Rudolph US Patent App. 17/570,542, 2022 | | 2022 |
Maja RudolphLead Research Scientist, Bosch Center for AIDirección de correo verificada de cs.columbia.edu
