Ikuti
Lars Maaløe
Lars Maaløe
Nama lainnyaLars Maaloe, Lars Maaloee
Co-Founder & CTO @ Corti | Adj. Assoc. Professor of Machine Learning @ DTU
Email yang diverifikasi di corti.ai
Judul
Dikutip oleh
Dikutip oleh
Tahun
Ladder variational autoencoders
CK Sønderby, T Raiko, L Maaløe, SK Sønderby, O Winther
Advances in Neural Information Processing Systems, 3738-3746, 2016
10132016
Auxiliary deep generative models
L Maaløe, CK Sønderby, SK Sønderby, O Winther
Proceedings of the International Conference on Machine Learning, 2016
5262016
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
4072022
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
2452019
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
1502016
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
812021
Recurrent spatial transformer networks
SK Sønderby, CK Sønderby, L Maaløe, O Winther
arXiv preprint arXiv:1509.05329, 2015
702015
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
452023
Semi-supervised generation with cluster-aware generative models
L Maaløe, M Fraccaro, O Winther
NIPS Workshop on Advances in Approximate Bayesian Inference, 2017
412017
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
352015
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
242022
Deep belief nets for topic modeling
L Maaløe, M Arngren, O Winther
ICML workshop on Knowledge-Powered Deep Learning for Text Mining, 2015
202015
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
132022
Condition Monitoring in Photovoltaic Systems by Semi-Supervised Machine Learning
L Maaløe, O Winther, S Spataru, D Sera
Energies 13 (3), 584, 2020
132020
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
132017
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
122016
Towards Hierarchical Discrete Variational Autoencoders
V Liévin, A Dittadi, L Maaløe, O Winther
NeurIPS Workshop on Advances in Approximate Bayesian Inference, 2019
112019
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
92021
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
82021
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
82018
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