Michael Gutmann

According to our database, Michael Gutmann authored at least 21 papers between 1996 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

An Extendable <i>Python</i> Implementation of Robust Optimization Monte Carlo.

Vasilis Gkolemis, Michael Gutmann, Henri Pesonen

J. Stat. Softw., 2024

2023

An Extendable Python Implementation of Robust Optimisation Monte Carlo.

Vasilis Gkolemis, Michael Gutmann, Henri Pesonen

CoRR, 2023

2020

Extending the statistical software package Engine for Likelihood-Free Inference.

Vasileios Gkolemis, Michael Gutmann

CoRR, 2020

2017

Simultaneous Estimation of Nongaussian Components and Their Correlation Structure.

Hiroaki Sasaki, Michael Gutmann, Hayaru Shouno, Aapo Hyvärinen

Neural Comput., 2017

2014

Estimating Dependency Structures for non-Gaussian Components with Linear and Energy Correlations.

Hiroaki Sasaki, Michael Gutmann, Hayaru Shouno, Aapo Hyvärinen

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013

Correlated topographic analysis: estimating an ordering of correlated components.

Hiroaki Sasaki, Michael Gutmann, Hayaru Shouno, Aapo Hyvärinen

Mach. Learn., 2013

2012

Topographic Analysis of Correlated Components.

Hiroaki Sasaki, Michael Gutmann, Hayaru Shouno, Aapo Hyvärinen

Proceedings of the 4th Asian Conference on Machine Learning, 2012

Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics.

Michael Gutmann, Aapo Hyvärinen

J. Mach. Learn. Res., 2012

A Family of Computationally Efficient and Simple Estimators for Unnormalized Statistical Models

Miika Pihlaja, Michael Gutmann, Aapo Hyvärinen

CoRR, 2012

Learning a selectivity-invariance-selectivity feature extraction architecture for images.

Michael Gutmann, Aapo Hyvärinen

Proceedings of the 21st International Conference on Pattern Recognition, 2012

2011

Bregman divergence as general framework to estimate unnormalized statistical models.

Michael Gutmann, Junichiro Hirayama

Proceedings of the UAI 2011, 2011

Complex-Valued Independent Component Analysis of Natural Images.

Valero Laparra, Michael Gutmann, Jesús Malo, Aapo Hyvärinen

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2011, 2011

Extracting Coactivated Features from Multiple Data Sets.

Michael Gutmann, Aapo Hyvärinen

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2011, 2011

2010

Noise-contrastive estimation: A new estimation principle for unnormalized statistical models.

Michael Gutmann, Aapo Hyvärinen

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

A Family of Computationally E cient and Simple Estimators for Unnormalized Statistical Models.

Miika Pihlaja, Michael Gutmann, Aapo Hyvärinen

Proceedings of the UAI 2010, 2010

2009

Learning Natural Image Structure with a Horizontal Product Model.

Urs Köster, Jussi T. Lindgren, Michael Gutmann, Aapo Hyvärinen

Proceedings of the Independent Component Analysis and Signal Separation, 2009

Learning Features by Contrasting Natural Images with Noise.

Michael Gutmann, Aapo Hyvärinen

Proceedings of the Artificial Neural Networks, 2009

Learning reconstruction and prediction of natural stimuli by a population of spiking neurons.

Michael Gutmann, Aapo Hyvärinen

Proceedings of the 17th European Symposium on Artificial Neural Networks, 2009

2008

Toward data representation with spiking neurons.

Michael Gutmann, Kazuyuki Aihara

Artif. Life Robotics, 2008

Learning encoding and decoding filters for data representation with a spiking neuron.

Michael Gutmann, Aapo Hyvärinen, Kazuyuki Aihara

Proceedings of the International Joint Conference on Neural Networks, 2008

1996

Linear controller for an inverted pendulum having restricted travel: A high-and-low gain approach.

Zongli Lin, Ali Saberi, Michael Gutmann, Yacov A. Shamash

Autom., 1996