20132019

Research output per year

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Research Output

  • 7 Conference contribution
  • 3 Working paper
  • 1 Article
  • 1 Doctoral Thesis
2019

Regularizing Trajectory Optimization with Denoising Autoencoders

Boney, R., Di Palo, N., Berglund, M., Ilin, A., Kannala, J., Rasmus, A. & Valpola, H., 2019, 33rd Conference on Neural Information Processing Systems: NeurIPS 2019 . Neural Information Processing Systems Foundation, (Advances in Neural Information Processing Systems).

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Open Access
2017

Unsupervised Networks, Stochasticity and Optimization in Deep Learning

Berglund, M., 2017, Aalto University. 214 p.

Research output: ThesisDoctoral ThesisCollection of Articles

Open Access
2016

Scalable gradient-based tuning of continuous regularization hyperparameters

Luketina, J., Berglund, M., Greff, K. & Raiko, T., 2016, 33rd International Conference on Machine Learning, ICML 2016. Vol. 6. p. 4333-4341 9 p.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

2 Citations (Scopus)

Stochastic gradient estimate variance in contrastive divergence and persistent contrastive divergence

Berglund, M., 1 Jan 2016, ESANN 2016 - 24th European Symposium on Artificial Neural Networks. p. 521-526 6 p.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)
2015

Bidirectional recurrent neural networks as generative models

Berglund, M., Raiko, T., Honkala, M., Kärkkäinen, L., Vetek, A. & Karhunen, J., 2015, Advances in Neural Information Processing Systems. Neural Information Processing Systems Foundation, Vol. 2015-January. p. 856-864 9 p.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

29 Citations (Scopus)

Measuring the usefulness of hidden units in Boltzmann machines with mutual information

Berglund, M., Raiko, T. & Cho, K., Apr 2015, In : Neural Networks. 64, p. 12-18 7 p.

Research output: Contribution to journalArticleScientificpeer-review

10 Citations (Scopus)

Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters

Luketina, J., Berglund, M. & Raiko, T., 2015.

Research output: Working paperProfessional

Semi-supervised learning with Ladder networks

Rasmus, A., Valpola, H., Honkala, M., Berglund, M. & Raiko, T., 2015, Advances in Neural Information Processing Systems. Neural Information Processing Systems Foundation, Vol. 2015-January. p. 3546-3554 9 p.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

348 Citations (Scopus)

Techniques for Learning Binary Stochastic Feedforward Neural Networks

Raiko, P., Berglund, M., Alain, G. & Dinh, L., 7 May 2015, International Conference on Learning Representations (ICLR 2015). p. 1-10 10 p.

Research output: Chapter in Book/Report/Conference proceedingConference contributionProfessional

Techniques for Learning Binary Stochastic Feedforward Neural Networks

Raiko, T., Berglund, M., Alain, G. & Dinh, L., 2014, ArXiv.

Research output: Working paperProfessional

2013

Measuring the Usefulness of Hidden Units in Boltzmann Machines with Mutual Information

Berglund, M., Raiko, T. & Cho, K., 2013, 20th International Conference, ICONIP 2013, Daegu, Korea, November 3-7, 2013. Proceedings, Part I. p. 482-489

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

2 Citations (Scopus)