| SimTS: rethinking contrastive representation learning for time series forecasting X Zheng, X Chen, M Schürch, A Mollaysa, A Allam, M Krauthammer arXiv preprint arXiv:2303.18205, 2023 | 30 | 2023 |
| Goal-directed generation of discrete structures with conditional generative models A Mollaysa, B Paige, A Kalousis Advances in Neural Information Processing Systems 33, 21923-21933, 2020 | 17 | 2020 |
| Regularising non-linear models using feature side-information A Mollaysa, P Strasser, A Kalousis International Conference on Machine Learning, 2508-2517, 2017 | 16 | 2017 |
| Learning to augment with feature side-information A Mollaysa, A Kalousis, E Bruno, M Diephuis Asian Conference on Machine Learning, 173-187, 2019 | 10 | 2019 |
| Mixed integer second-order cone programming for the horizontal and vertical free-flight planning problem Z Yuan, LA Moreno, A Fügenschuh, A Kaier, A Mollaysa, S Schlobach Helmut-Schmidt-Univ., Univ. der Bundeswehr Hamburg, 2015 | 7 | 2015 |
| Conditional generation of molecules from disentangled representations A Mollaysa, B Paige, A Kalousis | 4 | 2019 |
| Generative time series models with interpretable latent processes for complex disease trajectories C Trottet, M Schürch, A Mollaysa, A Allam, M Krauthammer Deep Generative Models for Health Workshop NeurIPS 2023, 2023 | 3 | 2023 |
| Generating Personalized Insulin Treatments Strategies with Conditional Generative Time Series Models M Schürch, X Li, A Allam, G Hofer, A Mollaysa, C Cavelti-Weder, ... Deep Generative Models for Health Workshop NeurIPS 2023, 2023 | 2 | 2023 |
| DDoS: A Graph Neural Network based Drug Synergy Prediction Algorithm K Schwarz, A Pliego-Mendieta, L Planas-Paz, C Pauli, A Allam, ... Conference on Health, Inference, and Learning (CHIL) 2024, 2022 | 2 | 2022 |
| Effective genome editing with an enhanced ISDra2 TnpB system and deep learning-predicted ωRNAs KF Marquart, N Mathis, A Mollaysa, S Müller, L Kissling, T Rothgangl, ... Nature Methods, 1-10, 2024 | 1 | 2024 |
| Generating Personalized Insulin Treatments Strategies with Deep Conditional Generative Time Series Models M Schürch, X Li, A Allam, G Rathmes, A Mollaysa, C Cavelti-Weder, ... arXiv preprint arXiv:2309.16521, 2023 | 1 | 2023 |
| Structural and Functional Regularization of Deep Learning Models M Aminanmu Université de Genève, 2021 | 1 | 2021 |
| Rethinking Molecular Design: Integrating Latent Variable and Auto-Regressive Models for Goal Directed Generation H Arthur-Loui, A Mollaysa, M Krauthammer arXiv preprint arXiv:2409.00046, 2024 | | 2024 |
| Integrating Latent Variable and Auto-Regressive Models for Goal-directed Molecule Generation A Mollaysa, H Arthur-Loui, M Krauthammer arXiv e-prints, arXiv: 2409.00046, 2024 | | 2024 |
| Rethinking Molecular Design: Integrating Latent Variable and Auto-Regressive Models for Enhanced Goal Directed Generation AL Heath, A Mollaysa, M Krauthammer ICML 2024 Workshop on Efficient and Accessible Foundation Models for …, 2024 | | 2024 |
| Simple Contrastive Representation Learning for Time Series Forecasting X Zheng, X Chen, M Schürch, A Mollaysa, A Allam, M Krauthammer ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and …, 2024 | | 2024 |
| Dynamic Local Attention with Hierarchical Patching for Irregular Clinical Time Series X Chen, X Zheng, A Mollaysa, M Schürch, A Allam, M Krauthammer arXiv preprint arXiv:2311.07744, 2023 | | 2023 |
| Attention-based Multi-task Learning for Base Editor Outcome Prediction A Mollaysa, A Allam, M Krauthammer arXiv preprint arXiv:2310.02919, 2023, 2023 | | 2023 |
| Conditional generation of molecules from disentangled representations M Amina, P Brooks, K Alexandros Neurips 2020, ML4Molecules, 2020 | | 2020 |
| Learning with feature side-information A Mollaysa, P Strasser, A Kalousis Learning in High Dimensions with Structure, Workshop proceedings of the 30th …, 2016 | | 2016 |
Michael KrauthammerUniversity of ZurichEmail verificata su uzh.ch
