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2020 – today
- 2024
[c34]Yasmine Nahal, Markus Heinonen, Mikhail Kabeshov, Jon Paul Janet, Eva Nittinger, Ola Engkvist, Samuel Kaski:
Towards Interpretable Models of Chemist Preferences for Human-in-the-Loop Assisted Drug Discovery. AIDD@ICANN 2024: 58-70- [c33]Muhammad Arslan Masood
, Samuel Kaski, Hugo Ceulemans, Dorota Herman, Markus Heinonen:
Balancing Imbalanced Toxicity Models: Using MolBERT with Focal Loss. AIDD@ICANN 2024: 82-97 - [c32]Trung Q. Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski:
Input-gradient space particle inference for neural network ensembles. ICLR 2024 - [c31]Yogesh Verma, Markus Heinonen, Vikas Garg:
ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs. ICLR 2024 - [i43]Alexandru Dumitrescu, Dani Korpela, Markus Heinonen, Yogesh Verma, Valerii Iakovlev, Vikas Garg, Harri Lähdesmäki:
Field-based Molecule Generation. CoRR abs/2402.15864 (2024) - [i42]Yogesh Verma, Markus Heinonen, Vikas Garg:
ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs. CoRR abs/2404.10024 (2024) - [i41]Najwa Laabid, Severi Rissanen, Markus Heinonen, Arno Solin, Vikas Garg:
Alignment is Key for Applying Diffusion Models to Retrosynthesis. CoRR abs/2405.17656 (2024) - [i40]Severi Rissanen, Markus Heinonen, Arno Solin:
Improving Discrete Diffusion Models via Structured Preferential Generation. CoRR abs/2405.17889 (2024) - [i39]Markus Heinonen, Ba-Hien Tran, Michael Kampffmeyer, Maurizio Filippone:
Robust Classification by Coupling Data Mollification with Label Smoothing. CoRR abs/2406.01494 (2024) - [i38]Trung Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski:
Improving robustness to corruptions with multiplicative weight perturbations. CoRR abs/2406.16540 (2024) - [i37]Rafal Karczewski, Samuel Kaski, Markus Heinonen, Vikas Garg:
What Ails Generative Structure-based Drug Design: Too Little or Too Much Expressivity? CoRR abs/2408.06050 (2024) - 2023
- [j13]Emmi Jokinen, Alexandru Dumitrescu, Jani Huuhtanen, Vladimir Gligorijevic, Satu Mustjoki, Richard Bonneau, Markus Heinonen, Harri Lähdesmäki:
TCRconv: predicting recognition between T cell receptors and epitopes using contextualized motifs. Bioinform. 39(1) (2023) - [j12]Magnus Ross, Markus Heinonen:
Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian Processes. Trans. Mach. Learn. Res. 2023 (2023) - [j11]Oliver Struckmeier, Ievgen Redko, Anton Mallasto, Karol Arndt, Markus Heinonen, Ville Kyrki:
Learning representations that are closed-form Monge mapping optimal with application to domain adaptation. Trans. Mach. Learn. Res. 2023 (2023) - [c30]Vishnu Raj, Tianyu Cui, Markus Heinonen, Pekka Marttinen:
Incorporating functional summary information in Bayesian neural networks using a Dirichlet process likelihood approach. AISTATS 2023: 6741-6763 - [c29]Valerii Iakovlev, Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki:
Latent Neural ODEs with Sparse Bayesian Multiple Shooting. ICLR 2023 - [c28]Severi Rissanen, Markus Heinonen, Arno Solin:
Generative Modelling with Inverse Heat Dissipation. ICLR 2023 - [c27]Yogesh Verma, Markus Heinonen, Vikas Garg:
AbODE: Ab initio antibody design using conjoined ODEs. ICML 2023: 35037-35050 - [c26]Giulio Franzese, Giulio Corallo, Simone Rossi, Markus Heinonen, Maurizio Filippone, Pietro Michiardi:
Continuous-Time Functional Diffusion Processes. NeurIPS 2023 - [c25]Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki:
Learning Space-Time Continuous Latent Neural PDEs from Partially Observed States. NeurIPS 2023 - [c24]Aarne Talman, Hande Çelikkanat, Sami Virpioja, Markus Heinonen, Jörg Tiedemann:
Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging. NoDaLiDa 2023: 358-365 - [i36]Giulio Franzese, Simone Rossi, Dario Rossi, Markus Heinonen, Maurizio Filippone, Pietro Michiardi:
Continuous-Time Functional Diffusion Processes. CoRR abs/2303.00800 (2023) - [i35]Magnus Ross, Markus Heinonen:
Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian Processes. CoRR abs/2303.01925 (2023) - [i34]Aarne Talman, Hande Çelikkanat, Sami Virpioja, Markus Heinonen, Jörg Tiedemann:
Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging. CoRR abs/2304.04726 (2023) - [i33]Oliver Struckmeier, Ievgen Redko, Anton Mallasto, Karol Arndt, Markus Heinonen, Ville Kyrki:
Beyond invariant representation learning: linearly alignable latent spaces for efficient closed-form domain adaptation. CoRR abs/2305.07500 (2023) - [i32]Yogesh Verma, Markus Heinonen, Vikas Garg:
AbODE: Ab Initio Antibody Design using Conjoined ODEs. CoRR abs/2306.01005 (2023) - [i31]Trung Q. Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski:
Input gradient diversity for neural network ensembles. CoRR abs/2306.02775 (2023) - [i30]Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki:
Learning Space-Time Continuous Neural PDEs from Partially Observed States. CoRR abs/2307.04110 (2023) - [i29]Quentin Bouniot, Ievgen Redko, Anton Mallasto, Charlotte Laclau, Karol Arndt, Oliver Struckmeier, Markus Heinonen, Ville Kyrki, Samuel Kaski:
Understanding deep neural networks through the lens of their non-linearity. CoRR abs/2310.11439 (2023) - 2022
- [j10]Anni A. Antikainen, Markus Heinonen, Harri Lähdesmäki:
Modeling binding specificities of transcription factor pairs with random forests. BMC Bioinform. 23(1): 212 (2022) - [j9]Alexander Aushev, Henri Pesonen, Markus Heinonen, Jukka Corander, Samuel Kaski:
Likelihood-free inference with deep Gaussian processes. Comput. Stat. Data Anal. 174: 107529 (2022) - [j8]Iiris Sundin, Alexey Voronov, Haoping Xiao, Kostas Papadopoulos, Esben Jannik Bjerrum, Markus Heinonen, Atanas Patronov, Samuel Kaski, Ola Engkvist:
Human-in-the-loop assisted de novo molecular design. J. Cheminformatics 14(1): 86 (2022) - [c23]Trung Q. Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski:
Tackling covariate shift with node-based Bayesian neural networks. ICML 2022: 21751-21775 - [c22]Yogesh Verma, Samuel Kaski, Markus Heinonen, Vikas Garg:
Modular Flows: Differential Molecular Generation. NeurIPS 2022 - [c21]Pashupati Hegde, Çagatay Yildiz, Harri Lähdesmäki, Samuel Kaski, Markus Heinonen:
Variational multiple shooting for Bayesian ODEs with Gaussian processes. UAI 2022: 790-799 - [i28]Trung Q. Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski:
Tackling covariate shift with node-based Bayesian neural networks. CoRR abs/2206.02435 (2022) - [i27]Severi Rissanen, Markus Heinonen, Arno Solin:
Generative Modelling With Inverse Heat Dissipation. CoRR abs/2206.13397 (2022) - [i26]Vishnu Raj, Tianyu Cui, Markus Heinonen, Pekka Marttinen:
Look beyond labels: Incorporating functional summary information in Bayesian neural networks. CoRR abs/2207.01234 (2022) - [i25]Valerii Iakovlev, Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki:
Latent Neural ODEs with Sparse Bayesian Multiple Shooting. CoRR abs/2210.03466 (2022) - [i24]Yogesh Verma, Samuel Kaski, Markus Heinonen, Vikas Garg:
Modular Flows: Differential Molecular Generation. CoRR abs/2210.06032 (2022) - [i23]David Duvenaud, Markus Heinonen, Michael Tiemann, Max Welling:
Differential Equations and Continuous-Time Deep Learning (Dagstuhl Seminar 22332). Dagstuhl Reports 12(8): 20-30 (2022) - 2021
- [j7]Emmi Jokinen, Jani Huuhtanen, Satu Mustjoki, Markus Heinonen, Harri Lähdesmäki:
Predicting recognition between T cell receptors and epitopes with TCRGP. PLoS Comput. Biol. 17(3) (2021) - [c20]Anton Mallasto, Markus Heinonen, Samuel Kaski:
Bayesian Inference for Optimal Transport with Stochastic Cost. ACML 2021: 1601-1616 - [c19]Simone Rossi, Markus Heinonen, Edwin V. Bonilla, Zheyang Shen, Maurizio Filippone:
Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations. AISTATS 2021: 1837-1845 - [c18]Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki:
Learning continuous-time PDEs from sparse data with graph neural networks. ICLR 2021 - [c17]Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki:
Continuous-time Model-based Reinforcement Learning. ICML 2021: 12009-12018 - [c16]Zheyang Shen, Markus Heinonen, Samuel Kaski:
De-randomizing MCMC dynamics with the diffusion Stein operator. NeurIPS 2021: 17507-17517 - [i22]Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki:
Continuous-Time Model-Based Reinforcement Learning. CoRR abs/2102.04764 (2021) - [i21]Anton Mallasto, Karol Arndt, Markus Heinonen, Samuel Kaski, Ville Kyrki:
Affine Transport for Sim-to-Real Domain Adaptation. CoRR abs/2105.11739 (2021) - [i20]Pashupati Hegde, Çagatay Yildiz, Harri Lähdesmäki, Samuel Kaski, Markus Heinonen:
Bayesian inference of ODEs with Gaussian processes. CoRR abs/2106.10905 (2021) - [i19]David Blanco Mulero, Markus Heinonen, Ville Kyrki:
Evolving-Graph Gaussian Processes. CoRR abs/2106.15127 (2021) - [i18]Zheyang Shen, Markus Heinonen, Samuel Kaski:
De-randomizing MCMC dynamics with the diffusion Stein operator. CoRR abs/2110.03768 (2021) - 2020
- [c15]Zheyang Shen, Markus Heinonen, Samuel Kaski:
Learning spectrograms with convolutional spectral kernels. AISTATS 2020: 3826-3836 - [i17]Simone Rossi, Markus Heinonen, Edwin V. Bonilla, Zheyang Shen, Maurizio Filippone:
Rethinking Sparse Gaussian Processes: Bayesian Approaches to Inducing-Variable Approximations. CoRR abs/2003.03080 (2020) - [i16]Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki:
Learning continuous-time PDEs from sparse data with graph neural networks. CoRR abs/2006.08956 (2020) - [i15]Alexander Aushev, Henri Pesonen, Markus Heinonen, Jukka Corander, Samuel Kaski:
Likelihood-Free Inference with Deep Gaussian Processes. CoRR abs/2006.10571 (2020) - [i14]Anton Mallasto, Markus Heinonen, Samuel Kaski:
Bayesian Inference for Optimal Transport with Stochastic Cost. CoRR abs/2010.09327 (2020) - [i13]Trung Q. Trinh, Samuel Kaski, Markus Heinonen:
Scalable Bayesian neural networks by layer-wise input augmentation. CoRR abs/2010.13498 (2020) - [i12]Charles W. L. Gadd, Markus Heinonen, Harri Lähdesmäki, Samuel Kaski:
Sample-efficient reinforcement learning using deep Gaussian processes. CoRR abs/2011.01226 (2020)
2010 – 2019
- 2019
- [j6]Markus Heinonen, Maria Osmala, Henrik Mannerström, Janne Wallenius, Samuel Kaski, Juho Rousu, Harri Lähdesmäki:
Bayesian metabolic flux analysis reveals intracellular flux couplings. Bioinform. 35(14): i548-i557 (2019) - [c14]Pashupati Hegde, Markus Heinonen, Harri Lähdesmäki, Samuel Kaski:
Deep learning with differential Gaussian process flows. AISTATS 2019: 1812-1821 - [c13]Zheyang Shen, Markus Heinonen, Samuel Kaski:
Harmonizable mixture kernels with variational Fourier features. AISTATS 2019: 3273-3282 - [c12]Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki:
ODE2VAE: Deep generative second order ODEs with Bayesian neural networks. NeurIPS 2019: 13412-13421 - [c11]Kenneth Blomqvist, Samuel Kaski, Markus Heinonen:
Deep Convolutional Gaussian Processes. ECML/PKDD (2) 2019: 582-597 - [i11]Zheyang Shen, Markus Heinonen, Samuel Kaski:
Learning spectrograms with convolutional spectral kernels. CoRR abs/1905.09917 (2019) - [i10]Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki:
ODE$^2$VAE: Deep generative second order ODEs with Bayesian neural networks. CoRR abs/1905.10994 (2019) - 2018
- [j5]Emmi Jokinen, Markus Heinonen, Harri Lähdesmäki:
mGPfusion: predicting protein stability changes with Gaussian process kernel learning and data fusion. Bioinform. 34(13): i274-i283 (2018) - [j4]Anna Cichonska, Tapio Pahikkala, Sándor Szedmák, Heli Julkunen, Antti Airola, Markus Heinonen, Tero Aittokallio, Juho Rousu:
Learning with multiple pairwise kernels for drug bioactivity prediction. Bioinform. 34(13): i509-i518 (2018) - [c10]Markus Heinonen, Çagatay Yildiz, Henrik Mannerström, Jukka Intosalmi, Harri Lähdesmäki:
Learning unknown ODE models with Gaussian processes. ICML 2018: 1964-1973 - [c9]Çagatay Yildiz, Markus Heinonen, Jukka Intosalmi, Henrik Mannerström, Harri Lähdesmäki:
Learning stochastic differential equations with Gaussian Processes without Gradient Matching. MLSP 2018: 1-6 - [c8]Pashupati Hegde, Markus Heinonen, Samuel Kaski:
Variational zero-inflated Gaussian processes with sparse kernels. UAI 2018: 361-371 - [i9]Markus Heinonen, Maria Osmala, Henrik Mannerström, Janne Wallenius, Samuel Kaski, Juho Rousu, Harri Lähdesmäki:
Bayesian Metabolic Flux Analysis reveals intracellular flux couplings. CoRR abs/1804.06673 (2018) - [i8]Çagatay Yildiz, Markus Heinonen, Jukka Intosalmi, Henrik Mannerström, Harri Lähdesmäki:
Learning Stochastic Differential Equations With Gaussian Processes Without Gradient Matching. CoRR abs/1807.05748 (2018) - [i7]Kenneth Blomqvist, Samuel Kaski, Markus Heinonen:
Deep convolutional Gaussian processes. CoRR abs/1810.03052 (2018) - [i6]Pashupati Hegde, Markus Heinonen, Harri Lähdesmäki, Samuel Kaski:
Deep learning with differential Gaussian process flows. CoRR abs/1810.04066 (2018) - [i5]Zheyang Shen, Markus Heinonen, Samuel Kaski:
Harmonizable mixture kernels with variational Fourier features. CoRR abs/1810.04416 (2018) - [i4]Sami Remes, Markus Heinonen, Samuel Kaski:
Neural Non-Stationary Spectral Kernel. CoRR abs/1811.10978 (2018) - 2017
- [c7]Sami Remes, Markus Heinonen, Samuel Kaski:
A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings. ACML 2017: 455-470 - [c6]Sami Remes, Markus Heinonen, Samuel Kaski:
Non-Stationary Spectral Kernels. NIPS 2017: 4642-4651 - [i3]Sami Remes, Markus Heinonen, Samuel Kaski:
Non-Stationary Spectral Kernels. CoRR abs/1705.08736 (2017) - 2016
- [c5]Romain Brault, Markus Heinonen, Florence d'Alché-Buc:
Random Fourier Features For Operator-Valued Kernels. ACML 2016: 110-125 - [c4]Markus Heinonen, Henrik Mannerström, Juho Rousu, Samuel Kaski, Harri Lähdesmäki:
Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo. AISTATS 2016: 732-740 - [i2]Romain Brault, Florence d'Alché-Buc, Markus Heinonen:
Random Fourier Features for Operator-Valued Kernels. CoRR abs/1605.02536 (2016) - 2015
- [j3]Markus Heinonen, Olivier Guipaud, Fabien Milliat, Valérie Buard, Béatrice Micheau, Georges Tarlet, Marc Benderitter, Farida Zehraoui, Florence d'Alché-Buc:
Detecting time periods of differential gene expression using Gaussian processes: an application to endothelial cells exposed to radiotherapy dose fraction. Bioinform. 31(5): 728-735 (2015) - 2014
- [i1]Markus Heinonen, Florence d'Alché-Buc:
Learning nonparametric differential equations with operator-valued kernels and gradient matching. CoRR abs/1411.5172 (2014) - 2012
- [b1]Markus Heinonen:
Computational methods for small molecules. University of Helsinki, Finland, 2012 - [j2]Markus Heinonen, Huibin Shen, Nicola Zamboni, Juho Rousu:
Metabolite identification and molecular fingerprint prediction through machine learning. Bioinform. 28(18): 2333-2341 (2012) - [c3]Markus Heinonen, Niko Välimäki, Veli Mäkinen, Juho Rousu:
Efficient Path Kernels for Reaction Function Prediction. BIOINFORMATICS 2012: 202-207 - 2011
- [j1]Markus Heinonen, Sampsa Lappalainen, Taneli Mielikäinen, Juho Rousu:
Computing Atom Mappings for Biochemical Reactions without Subgraph Isomorphism. J. Comput. Biol. 18(1): 43-58 (2011) - 2010
- [c2]Hongyu Su, Markus Heinonen, Juho Rousu:
Structured Output Prediction of Anti-cancer Drug Activity. PRIB 2010: 38-49
2000 – 2009
- 2006
- [c1]Markus Heinonen, Ari Rantanen, Taneli Mielikäinen, Esa Pitkänen, Juha Kokkonen, Juho Rousu:
Ab Initio Prediction of Molecular Fragments from Tandem Mass Spectrometry Data. German Conference on Bioinformatics 2006: 40-53
Coauthor Index
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