Professorship Lähdesmäki Harri

Filter
Conference contribution

Search results

  • 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.). 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

    Open Access
    1 Citation (Scopus)
  • Enriched mixtures of generalised Gaussian process experts

    Gadd, C. W. L., Wade, S. & Boukouvalas, A., 2020, INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 108. Chiappa, S. & Calandra, R. (eds.). p. 3144-3153 10 p. (Proceedings of Machine Learning Research; vol. 108).

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

    Open Access
    File
    8 Downloads (Pure)
  • Learning spectrograms with convolutional spectral kernels

    Shen, Z., Heinonen, M. & Kaski, S., 2020, The 23rd International Conference on Artificial Intelligence and Statistics. Chiappa, S. & Calandra, R. (eds.). p. 3826-3836 10 p. (Proceedings of Machine Learning Research; vol. 108).

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

    Open Access
    File
    4 Downloads (Pure)
  • LuxHS: DNA Methylation Analysis with Spatially Varying Correlation Structure

    Halla-aho, V. & Lähdesmäki, H., 30 Apr 2020, Bioinformatics and Biomedical Engineering - 8th International Work-Conference, IWBBIO 2020, Proceedings. Rojas, I., Valenzuela, O., Rojas, F., Herrera, L. J. & Ortuño, F. (eds.). p. 505-516 12 p. (Lecture Notes in Computer Science ; vol. 12108).

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

  • 2019

    A Mathematical Model for Enhancer Activation Kinetics During Cell Differentiation

    Nousiainen, K., Intosalmi, J. & Lähdesmäki, H., 1 Jan 2019, Algorithms for Computational Biology - 6th International Conference, AlCoB 2019, Proceedings. Vega-Rodríguez, M. A., Holmes, I. & Martín-Vide, C. (eds.). p. 191-202 12 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11488 LNBI).

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

  • 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
    File
    36 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; vol. 32).

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

    Open Access
    3 Citations (Scopus)
  • TimeRank: A random walk approach for community discovery in dynamic networks

    Sarantopoulos, I., Papatheodorou, D., Vogiatzis, D., Tzortzis, G. & Paliouras, G., 1 Jan 2019, Complex Networks and Their Applications VII - Volume 1 Proceedings The 7th International Conference on Complex Networks and their Applications COMPLEX NETWORKS 2018. Lambiotte, R., Rocha, L. M., Lió, P., Cherifi, H., Aiello, L. M. & Cherifi, C. (eds.). p. 338-350 13 p. (Studies in Computational Intelligence; vol. 812).

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

    1 Citation (Scopus)
  • 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

  • Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization

    Simsekli, U., Yildiz, C., Nguyen, T. H., Richard, G. & Cemgil, A. T., 2018, Proceedings of the 35th International Conference on Machine Learning. p. 4681-4690 (Proceedings of Machine Learning Research; vol. 80).

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

    Open Access
    File
    9 Downloads (Pure)
  • Asynchronous stochastic Quasi-Newton MCMC for non-convex optimization supplementary document

    Simsekli, U., Yildiz, C., Nguyen, T. H., Richard, G. & Cemgil, A. T., 1 Jan 2018, 35th International Conference on Machine Learning, ICML 2018. Krause, A. & Dy, J. (eds.). Vol. 11. p. 4674-4683 8 p. (Proceedings of Machine Learning Research; vol. 80).

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

    Open Access
    File
    16 Downloads (Pure)
  • 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

    4 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)
    30 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
    45 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
    15 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
    87 Downloads (Pure)
  • 2015

    A probabilistic method for quantifying chromatin interactions

    Halla-Aho, V., Mannerström, H. & Lähdesmäki, H., 2015, Machine Learning in Computational Biology: A workshop at the Annual Conference on Neural Information Processing Systems (NIPS 2015) .

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