Markus Heinonen

According to our database, Markus Heinonen authored at least 60 papers between 2006 and 2024.

Collaborative distances:

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

2024

Field-based Molecule Generation.

Alexandru Dumitrescu, Dani Korpela, Markus Heinonen, Yogesh Verma, Valerii Iakovlev, Vikas Garg, Harri Lähdesmäki

CoRR, 2024

2023

TCRconv: predicting recognition between T cell receptors and epitopes using contextualized motifs.

Emmi Jokinen, Alexandru Dumitrescu, Jani Huuhtanen, Vladimir Gligorijevic, Satu Mustjoki, Richard Bonneau, Markus Heinonen, Harri Lähdesmäki

Bioinform., January, 2023

Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian Processes.

Magnus Ross, Markus Heinonen

Trans. Mach. Learn. Res., 2023

Understanding deep neural networks through the lens of their non-linearity.

Quentin Bouniot, Ievgen Redko, Anton Mallasto, Charlotte Laclau, Karol Arndt, Oliver Struckmeier, Markus Heinonen, Ville Kyrki, Samuel Kaski

CoRR, 2023

Learning Space-Time Continuous Neural PDEs from Partially Observed States.

Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki

CoRR, 2023

Input gradient diversity for neural network ensembles.

Trung Q. Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski

CoRR, 2023

Beyond invariant representation learning: linearly alignable latent spaces for efficient closed-form domain adaptation.

Oliver Struckmeier, Ievgen Redko, Anton Mallasto, Karol Arndt, Markus Heinonen, Ville Kyrki

CoRR, 2023

Continuous-Time Functional Diffusion Processes.

Giulio Franzese, Simone Rossi, Dario Rossi, Markus Heinonen, Maurizio Filippone, Pietro Michiardi

CoRR, 2023

Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging.

Aarne Talman, Hande Çelikkanat, Sami Virpioja, Markus Heinonen, Jörg Tiedemann

Proceedings of the 24th Nordic Conference on Computational Linguistics, 2023

Learning Space-Time Continuous Latent Neural PDEs from Partially Observed States.

Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Continuous-Time Functional Diffusion Processes.

Giulio Franzese, Giulio Corallo, Simone Rossi, Markus Heinonen, Maurizio Filippone, Pietro Michiardi

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

AbODE: Ab initio antibody design using conjoined ODEs.

Yogesh Verma, Markus Heinonen, Vikas Garg

Proceedings of the International Conference on Machine Learning, 2023

Generative Modelling with Inverse Heat Dissipation.

Severi Rissanen, Markus Heinonen, Arno Solin

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Latent Neural ODEs with Sparse Bayesian Multiple Shooting.

Valerii Iakovlev, Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Incorporating functional summary information in Bayesian neural networks using a Dirichlet process likelihood approach.

Vishnu Raj, Tianyu Cui, Markus Heinonen, Pekka Marttinen

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Modeling binding specificities of transcription factor pairs with random forests.

Anni A. Antikainen, Markus Heinonen, Harri Lähdesmäki

BMC Bioinform., December, 2022

Human-in-the-loop assisted de novo molecular design.

Iiris Sundin, Alexey Voronov, Haoping Xiao, Kostas Papadopoulos, Esben Jannik Bjerrum, Markus Heinonen, Atanas Patronov, Samuel Kaski, Ola Engkvist

J. Cheminformatics, 2022

Differential Equations and Continuous-Time Deep Learning (Dagstuhl Seminar 22332).

David Duvenaud, Markus Heinonen, Michael Tiemann, Max Welling

Dagstuhl Reports, 2022

Likelihood-free inference with deep Gaussian processes.

Alexander Aushev, Henri Pesonen, Markus Heinonen, Jukka Corander, Samuel Kaski

Comput. Stat. Data Anal., 2022

Look beyond labels: Incorporating functional summary information in Bayesian neural networks.

Vishnu Raj, Tianyu Cui, Markus Heinonen, Pekka Marttinen

CoRR, 2022

Variational multiple shooting for Bayesian ODEs with Gaussian processes.

Pashupati Hegde, Çagatay Yildiz, Harri Lähdesmäki, Samuel Kaski, Markus Heinonen

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Modular Flows: Differential Molecular Generation.

Yogesh Verma, Samuel Kaski, Markus Heinonen, Vikas K. Garg

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Tackling covariate shift with node-based Bayesian neural networks.

Trung Q. Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski

Proceedings of the International Conference on Machine Learning, 2022

2021

Predicting recognition between T cell receptors and epitopes with TCRGP.

Emmi Jokinen, Jani Huuhtanen, Satu Mustjoki, Markus Heinonen, Harri Lähdesmäki

PLoS Comput. Biol., 2021

Evolving-Graph Gaussian Processes.

David Blanco Mulero, Markus Heinonen, Ville Kyrki

CoRR, 2021

Bayesian inference of ODEs with Gaussian processes.

Pashupati Hegde, Çagatay Yildiz, Harri Lähdesmäki, Samuel Kaski, Markus Heinonen

CoRR, 2021

Affine Transport for Sim-to-Real Domain Adaptation.

Anton Mallasto, Karol Arndt, Markus Heinonen, Samuel Kaski, Ville Kyrki

CoRR, 2021

De-randomizing MCMC dynamics with the diffusion Stein operator.

Zheyang Shen, Markus Heinonen, Samuel Kaski

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Continuous-time Model-based Reinforcement Learning.

Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki

Proceedings of the 38th International Conference on Machine Learning, 2021

Learning continuous-time PDEs from sparse data with graph neural networks.

Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki

Proceedings of the 9th International Conference on Learning Representations, 2021

Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations.

Simone Rossi, Markus Heinonen, Edwin V. Bonilla, Zheyang Shen, Maurizio Filippone

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Bayesian Inference for Optimal Transport with Stochastic Cost.

Anton Mallasto, Markus Heinonen, Samuel Kaski

Proceedings of the Asian Conference on Machine Learning, 2021

2020

Sample-efficient reinforcement learning using deep Gaussian processes.

Charles W. L. Gadd, Markus Heinonen, Harri Lähdesmäki, Samuel Kaski

CoRR, 2020

Scalable Bayesian neural networks by layer-wise input augmentation.

Trung Q. Trinh, Samuel Kaski, Markus Heinonen

CoRR, 2020

Rethinking Sparse Gaussian Processes: Bayesian Approaches to Inducing-Variable Approximations.

Simone Rossi, Markus Heinonen, Edwin V. Bonilla, Zheyang Shen, Maurizio Filippone

CoRR, 2020

Learning spectrograms with convolutional spectral kernels.

Zheyang Shen, Markus Heinonen, Samuel Kaski

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

ODE$^2$VAE: Deep generative second order ODEs with Bayesian neural networks.

Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki

CoRR, 2019

Bayesian metabolic flux analysis reveals intracellular flux couplings.

Markus Heinonen, Maria Osmala, Henrik Mannerström, Janne Wallenius, Samuel Kaski, Juho Rousu, Harri Lähdesmäki

Bioinform., 2019

Deep Convolutional Gaussian Processes.

Kenneth Blomqvist, Samuel Kaski, Markus Heinonen

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

ODE2VAE: Deep generative second order ODEs with Bayesian neural networks.

Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Harmonizable mixture kernels with variational Fourier features.

Zheyang Shen, Markus Heinonen, Samuel Kaski

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Deep learning with differential Gaussian process flows.

Pashupati Hegde, Markus Heinonen, Harri Lähdesmäki, Samuel Kaski

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Neural Non-Stationary Spectral Kernel.

Sami Remes, Markus Heinonen, Samuel Kaski

CoRR, 2018

mGPfusion: predicting protein stability changes with Gaussian process kernel learning and data fusion.

Emmi Jokinen, Markus Heinonen, Harri Lähdesmäki

Bioinform., 2018

Learning with multiple pairwise kernels for drug bioactivity prediction.

Anna Cichonska, Tapio Pahikkala, Sándor Szedmák, Heli Julkunen, Antti Airola, Markus Heinonen, Tero Aittokallio, Juho Rousu

Bioinform., 2018

Variational zero-inflated Gaussian processes with sparse kernels.

Pashupati Hegde, Markus Heinonen, Samuel Kaski

Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Learning stochastic differential equations with Gaussian Processes without Gradient Matching.

Çagatay Yildiz, Markus Heinonen, Jukka Intosalmi, Henrik Mannerström, Harri Lähdesmäki

Proceedings of the 28th IEEE International Workshop on Machine Learning for Signal Processing, 2018

Learning unknown ODE models with Gaussian processes.

Markus Heinonen, Çagatay Yildiz, Henrik Mannerström, Jukka Intosalmi, Harri Lähdesmäki

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Non-Stationary Spectral Kernels.

Sami Remes, Markus Heinonen, Samuel Kaski

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings.

Sami Remes, Markus Heinonen, Samuel Kaski

Proceedings of The 9th Asian Conference on Machine Learning, 2017

2016

Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo.

Markus Heinonen, Henrik Mannerström, Juho Rousu, Samuel Kaski, Harri Lähdesmäki

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Random Fourier Features For Operator-Valued Kernels.

Romain Brault, Markus Heinonen, Florence d'Alché-Buc

Proceedings of The 8th Asian Conference on Machine Learning, 2016

2015

Detecting time periods of differential gene expression using Gaussian processes: an application to endothelial cells exposed to radiotherapy dose fraction.

Markus Heinonen, Olivier Guipaud, Fabien Milliat, Valérie Buard, Béatrice Micheau, Georges Tarlet, Marc Benderitter, Farida Zehraoui, Florence d'Alché-Buc

Bioinform., 2015

2014

Learning nonparametric differential equations with operator-valued kernels and gradient matching.

Markus Heinonen, Florence d'Alché-Buc

CoRR, 2014

2012

Computational methods for small molecules.

Markus Heinonen

PhD thesis, 2012

Metabolite identification and molecular fingerprint prediction through machine learning.

Markus Heinonen, Huibin Shen, Nicola Zamboni, Juho Rousu

Bioinform., 2012

Efficient Path Kernels for Reaction Function Prediction.

Markus Heinonen, Niko Välimäki, Veli Mäkinen, Juho Rousu

Proceedings of the BIOINFORMATICS 2012 - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms, Vilamoura, Algarve, Portugal, 1, 2012

2011

Computing Atom Mappings for Biochemical Reactions without Subgraph Isomorphism.

Markus Heinonen, Sampsa Lappalainen, Taneli Mielikäinen, Juho Rousu

J. Comput. Biol., 2011

2010

Structured Output Prediction of Anti-cancer Drug Activity.

Hongyu Su, Markus Heinonen, Juho Rousu

Proceedings of the Pattern Recognition in Bioinformatics, 2010

2006

Ab Initio Prediction of Molecular Fragments from Tandem Mass Spectrometry Data.

Markus Heinonen, Ari Rantanen, Taneli Mielikäinen, Esa Pitkänen, Juha Kokkonen, Juho Rousu

Proceedings of the German Conference on Bioinformatics GCB 2006, 19.09. 2006, 2006