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Conference contribution
2020

Deep Convolutional Gaussian Processes

Blomqvist, K., Kaski, S. & Heinonen, M., 1 Jan 2020, Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings. Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M. & Robardet, C. (eds.). SPRINGER , p. 582-597 16 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11907 LNAI).

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

Learning spectrograms with convolutional spectral kernels

Shen, Z., Heinonen, M. & Kaski, S., 2020, (Accepted/In press) The 23rd International Conference on Artificial Intelligence and Statistic. (Proceedings of Machine Learning Research).

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

2019

Deep convolutional Gaussian process

Blomqvist, K., Kaski, S. & Heinonen, M., 2019, (Accepted/In press) Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases.

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

Open Access

Deep learning with differential Gaussian process flows

Hegde, P., Heinonen, M., Lähdesmäki, H. & Kaski, S., Apr 2019, The 22nd International Conference on Artificial Intelligence and Statistic. Vol. 89. p. 1-15 16 p. (Proceedings of Machine Learning Research; vol. 89).

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

Open Access
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27 Downloads (Pure)

Harmonizable mixture kernels with variational Fourier features

Shen, Z., Heinonen, M. & Kaski, S., May 2019, The 22nd International Conference on Artificial Intelligence and Statistic. p. 1812-1821 (Proceedings of Machine Learning Research; vol. 89).

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

Open Access
File
23 Downloads (Pure)

ODE2VAE: Deep generative second order ODEs with Bayesian neural networks

Yildiz, C., Heinonen, M. & Lähdesmäki, 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
2018

A Nonparametric Spatio-temporal SDE Model

Yildiz, C., Heinonen, M. & Lähdesmäki, H., 2018, NIPS 2018 Spatiotemporal Workshop: 32nd Conference on Neural Information Processing Systems (NIPS 2018), Montréal, Canada. Neural Information Processing Systems Foundation, p. 1-5

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

Learning Stochastic Differential Equations With Gaussian Processes Without Gradient Matching

Yildiz, C., Heinonen, M., Intosalmi, J., Mannerström, H. & Lähdesmäki, H., 2018, IEEE International Workshop on Machine Learning for Signal Processing. IEEE, 6 p. 8516991

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

3 Citations (Scopus)

Learning unknown ODE models with Gaussian processes

Heinonen, M., Yildiz, C., Mannerström, H., Intosalmi, J. & Lähdesmäki, H., 2018, Proceedings of the 35th International Conference on Machine Learning, ICML 2018. Vol. 5. p. 3120-3132 13 p. (Proceedings of Machine Learning Research; vol. 80).

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

Open Access
File
1 Citation (Scopus)
19 Downloads (Pure)

Variational zero-inflated Gaussian processes with sparse kernels

Hegde, P., Heinonen, M. & Kaski, S., 2018, 34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018. AUAI Press, Vol. 1. p. 361-371 148

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

Open Access
2017

A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings

Remes, S., Heinonen, M. & Kaski, S., Nov 2017, Proceedings of the 9th Asian Conference on Machine Learning. Zhang, M-L. & Noh, Y-K. (eds.). p. 455-470 16 p. (Proceedings of Machine Learning Research; vol. 77).

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

Open Access
File
34 Downloads (Pure)

Non-Stationary Spectral Kernels

Remes, S., Heinonen, M. & Kaski, S., 2017, Advances in Neural Information Processing Systems 30: Proceedings of NIPS2017. Curran Associates, Inc., p. 4645-4654 (Advances in Neural Information Processing Systems; vol. 30).

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

Open Access
12 Citations (Scopus)
2016

Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo

Heinonen, M., Mannerström, H., Rousu, J., Kaski, S. & Lähdesmäki, H., 2016, Proceedings of the 19th International Conference on Artificial Intelligence and Statistics: JMLR: W&CP. JMLR, p. 732-740 9 p. ( JMLR: Workshop and Conference Proceedings; vol. 51).

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

Random Fourier Features For Operator-Valued Kernels

Brault, R., Heinonen, M. & d'Alché-Buc, F., 2016, Proceedings of the 8th Asian Conference on Machine Learning. Durrant, B. & Kim, K-E. (eds.). p. 110-125 (Proceedings of Machine Learning Research; vol. 63).

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

Open Access
File
80 Downloads (Pure)
2012

Efficient Path Kernels for Reaction Function Prediction

Heinonen, M., Välimäki, N., Mäkinen, V. & Rousu, J., 2012, Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms. p. 202-207

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

2010

Multilabel classification of drug-like molecules via max-margin conditional random fields

Su, H., Heinonen, M. & Rousu, J., 2010, Proceedings of The Fifth European Workshop on Probabilistic Graphical Models (PGM-2010): 13-15 September, 2010, Helsinki, Finland. Myllymäki, P., Roos, T. & Jaakkola, T. (eds.). HIIT, p. 265-272 (HIIT Publications; vol. 2010, no. 2).

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

Structured output prediction of anti-cancer drug activity

Su, H., Heinonen, M. & Rousu, J., 2010, Pattern Recognition in Bioinformatics: 5th IAPR International Conference, PRIB 2010, Nijmegen, The Netherlands, September 22-24, 2010. Proceedings. Dijkstra, T. M. H., Tsivtsivadze, E., Marchiori, E. & Heskes, T. (eds.). Springer-Verlag, p. 38-49 ( Lecture Notes in Computer Science; vol. 6282).

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

2006

Ab Initio prediction of molecular fragments from tandem mass spectrometry data

Heinonen, M., Rantanen, A., Mielikäinen, T., Pitkänen, E., Kokkonen, J. T. & Rousu, J., 2006, Proceedings of the German Conference on Bioinformatics. Huson, D., Kohlbacher, O., Lupas, A., Nieselt, K. & Zell, A. (eds.). Gesellschaft für Informatik (GI), p. 40-53 (Lecture Notes in Informatics (LNI); vol. P83).

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