| Ladder variational autoencoders CK Sønderby, T Raiko, L Maaløe, SK Sønderby, O Winther Advances in Neural Information Processing Systems, 3738-3746, 2016 | 898 | 2016 |
| Auxiliary deep generative models L Maaløe, CK Sønderby, SK Sønderby, O Winther Proceedings of the International Conference on Machine Learning, 2016 | 505 | 2016 |
| Self-Supervised Speech Representation Learning: A Review A Mohamed, H Lee, L Borgholt, JD Havtorn, J Edin, C Igel, K Kirchhoff, ... IEEE Journal of Selected Topics in Signal Processing 16 (6), 1179-1210, 2022 | 228 | 2022 |
| BIVA: A very deep hierarchy of latent variables for generative modeling L Maaløe, M Fraccaro, V Liévin, O Winther Advances in Neural Information Processing Systems, 2019 | 210 | 2019 |
| How to train deep variational autoencoders and probabilistic ladder networks CK Sønderby, T Raiko, L Maaløe, SK Sønderby, O Winther arXiv preprint arXiv:1602.02282, 2016 | 139 | 2016 |
| Recurrent spatial transformer networks SK Sønderby, CK Sønderby, L Maaløe, O Winther arXiv preprint arXiv:1509.05329, 2015 | 69 | 2015 |
| Hierarchical VAEs Know What They Don't Know JD Havtorn, J Frellsen, S Hauberg, L Maaløe Proceedings of the International Conference on Machine Learning, 2021 | 64 | 2021 |
| Semi-supervised generation with cluster-aware generative models L Maaløe, M Fraccaro, O Winther NIPS Workshop on Advances in Approximate Bayesian Inference, 2017 | 41 | 2017 |
| Improving semi-supervised learning with auxiliary deep generative models L Maaløe, CK Sønderby, SK Sønderby, O Winther NIPS Workshop on Advances in Approximate Bayesian Inference, 2015 | 31 | 2015 |
| Deep belief nets for topic modeling L Maaløe, M Arngren, O Winther ICML workshop on Knowledge-Powered Deep Learning for Text Mining, 2015 | 21 | 2015 |
| Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Study J Edin, A Junge, JD Havtorn, L Borgholt, M Maistro, T Ruotsalo, L Maaløe 46th International ACM SIGIR Conference on Research and Development in …, 2023 | 14 | 2023 |
| Utilizing Domain Knowledge in End-to-End Audio Processing TMS Tax, JLD Antich, H Purwins, L Maaløe NIPS workshop on machine learning for audio, 2017 | 13 | 2017 |
| Model-agnostic out-of-distribution detection using combined statistical tests F Bergamin, PA Mattei, JD Havtorn, H Senetaire, H Schmutz, L Maaløe, ... International Conference on Artificial Intelligence and Statistics (AISTATS), 2022 | 12 | 2022 |
| Condition Monitoring in Photovoltaic Systems by Semi-Supervised Machine Learning L Maaløe, O Winther, S Spataru, D Sera Energies 13 (3), 584, 2020 | 10 | 2020 |
| A brief overview of unsupervised neural speech representation learning L Borgholt, JD Havtorn, J Edin, L Maaløe, C Igel arXiv preprint arXiv:2203.01829, 2022 | 9 | 2022 |
| Towards Hierarchical Discrete Variational Autoencoders V Liévin, A Dittadi, L Maaløe, O Winther NeurIPS Workshop on Advances in Approximate Bayesian Inference, 2019 | 9 | 2019 |
| Do end-to-end speech recognition models care about context? L Borgholt, JD Havtorn, Ž Agić, A Søgaard, L Maaløe, C Igel INTERSPEECH 2020, 2021 | 8 | 2021 |
| Development and implementation of a PV performance monitoring system based on inverter measurements SV Spataru, A Gavriluta, D Sera, L Maaloe, O Winther 2016 IEEE Energy Conversion Congress and Exposition (ECCE), 1-7, 2016 | 8 | 2016 |
| Do we still need automatic speech recognition for spoken language understanding? L Borgholt, JD Havtorn, M Abdou, J Edin, L Maaløe, A Søgaard, C Igel arXiv preprint arXiv:2111.14842, 2021 | 6 | 2021 |
| On the Inductive Bias of Word-Character-Level Multi-Task Learning for Speech Recognition J Kremer, L Borgholt, L Maaløe NIPS Workshop on Interpretability and Robustness for Audio, Speech and Language, 2018 | 6 | 2018 |
Ole WintherBiology, Univ of Copenhagen, Genomic Medicine, Rigshospitalet and Technical University of Denmarkbio.ku.dk의 이메일 확인됨
