Lars Maaløe
Lars Maaløe
Co-Founder & CTO at Corti, PhD in Machine Learning
Bekræftet mail på cortilabs.com
Titel
Citeret af
Citeret af
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Ladder variational autoencoders
CK Sønderby, T Raiko, L Maaløe, SK Sønderby, O Winther
Advances in Neural Information Processing Systems, 3738-3746, 2016
4032016
Auxiliary deep generative models
L Maaløe, CK Sønderby, SK Sønderby, O Winther
Proceedings of the International Conference on Machine Learning, 2016
3282016
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
952016
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
452019
Recurrent spatial transformer networks
SK Sønderby, CK Sønderby, L Maaløe, O Winther
arXiv preprint arXiv:1509.05329, 2015
352015
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
282015
Semi-supervised generation with cluster-aware generative models
L Maaløe, M Fraccaro, O Winther
NIPS Workshop on Advances in Approximate Bayesian Inference, 2017
242017
Deep belief nets for topic modeling
L Maaløe, M Arngren, O Winther
ICML workshop on Knowledge-Powered Deep Learning for Text Mining, 2015
152015
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
62017
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
62016
Towards Hierarchical Discrete Variational Autoencoders
V Liévin, A Dittadi, L Maaløe, O Winther
NeurIPS Workshop on Advances in Approximate Bayesian Inference, 2019
42019
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
32018
CaGeM: A Cluster Aware Deep Generative Model
L Maaløe, M Fraccaro, O Winther
NIPS Workshop on Advances in Approximate Bayesian Inferences, 2017
32017
Do End-to-End Speech Recognition Models Care About Context?
L Borgholt, JD Havtorn, AS ˇZeljko Agic, L Maaløe, C Igel
Proc. Interspeech 2020, 4352-4356, 2020
12020
Condition Monitoring in Photovoltaic Systems by Semi-Supervised Machine Learning
L Maaløe, O Winther, S Spataru, D Sera
Energies 13 (3), 584, 2020
12020
MultiQT: Multimodal Learning for Real-Time Question Tracking in Speech
J Drachmann Havtorn, J Latko, J Edin, L Borgholt, L Maaløe, L Belgrano, ...
Association for Computational Linguistics, 2020
2020
Deep Generative Models for Semi-Supervised Machine Learning
L Maaløe
DTU Compute, 2018
2018
Exploiting Nontrivial Connectivity for Automatic Speech Recognition
M Paraschiv, L Borgholt, TMS Tax, M Singh, L Maaløe
NIPS workshop on machine learning for audio, 2017
2017
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