Markus Heinonen

发表

Mass Spectrometry Informatics in Systems Biology

Juho Rousu, Masanori Arita, Markus Heinonen, 2010 .

Scalable Bayesian neural networks by layer-wise input augmentation pdf

Samuel Kaski, Markus Heinonen, Trung Trinh, 2020, ArXiv.

Learning spectrograms with convolutional spectral kernels

Samuel Kaski, Markus Heinonen, Zheyang Shen, 2020, AISTATS.

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

Maurizio Filippone, Edwin V. Bonilla, Markus Heinonen, 2020, ArXiv.

Efficient Path Kernels for Reaction Function Prediction

Juho Rousu, Veli Mäkinen, Niko Välimäki, 2012, BIOINFORMATICS.

Computing Atom Mappings for Biochemical Reactions without Subgraph Isomorphism

Juho Rousu, Taneli Mielikäinen, Markus Heinonen, 2011, J. Comput. Biol..

Variational zero-inflated Gaussian processes with sparse kernels

Samuel Kaski, Markus Heinonen, Pashupati Hegde, 2018, UAI.

Metabolite identification and molecular fingerprint prediction through machine learning

Juho Rousu, Markus Heinonen, Nicola Zamboni, 2012, Bioinform..

Likelihood-Free Inference with Deep Gaussian Processes pdf

Samuel Kaski, Markus Heinonen, Jukka Corander, 2020, ArXiv.

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

Markus Heinonen, Florence d'Alché-Buc, Farida Zehraoui, 2015, Bioinform..

LEARNING STOCHASTIC DIFFERENTIAL EQUATIONS WITH GAUSSIAN PROCESSES WITHOUT GRADIENT MATCHING

Markus Heinonen, Henrik Mannerström, Jukka Intosalmi, 2018, 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP).

Computational methods for small molecules

Markus Heinonen, Markus Heinonen, 2012 .

Deep learning with differential Gaussian process flows pdf

Samuel Kaski, Markus Heinonen, Harri Lähdesmäki, 2018, AISTATS.

A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings

Samuel Kaski, Markus Heinonen, Sami Remes, 2017, ACML.

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

Markus Heinonen, Florence d'Alché-Buc, Florence d'Alché-Buc, 2014, ArXiv.

Determining epitope specificity of T cell receptors with TCRGP

Markus Heinonen, Harri Lähdesmäki, Emmi Jokinen, 2019, bioRxiv.

MSiB Mass Spectrometry Informatics in Systems Biology, Abstracts of the Workshop, October 28-29, 2010, Helsinki, Finland

Juho Rousu, Masanori Arita, Markus Heinonen, 2010 .

Non-Stationary Spectral Kernels

Samuel Kaski, Markus Heinonen, Sami Remes, 2017, NIPS.

Adoption of VR and AR technologies in the enterprise

Markus Heinonen, Markus Heinonen, 2017 .

Bayesian Inference for Optimal Transport with Stochastic Cost pdf

Samuel Kaski, Markus Heinonen, Anton Mallasto, 2020, ArXiv.

Neural Non-Stationary Spectral Kernel pdf

Samuel Kaski, Markus Heinonen, Sami Remes, 2018, ArXiv.

Random Fourier Features For Operator-Valued Kernels pdf

Markus Heinonen, Florence d'Alché-Buc, Romain Brault, 2016, ACML.

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

Markus Heinonen, Harri Lähdesmäki, Emmi Jokinen, 2018, Bioinform..

Harmonizable mixture kernels with variational Fourier features

Samuel Kaski, Markus Heinonen, Zheyang Shen, 2019, AISTATS.

Deep convolutional Gaussian process

Samuel Kaski, Markus Heinonen, Kenneth Blomqvist, 2018 .

Full waveform forward seismic modeling of geologically complex environment: Comparison of simulated and field seismic data

Markus Heinonen, Emilia Koivisto, Suvi Heinonen, 2012 .

Structured Output Prediction of Anti-cancer Drug Activity

Juho Rousu, Hongyu Su, Markus Heinonen, 2010, PRIB.

Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo

Juho Rousu, Samuel Kaski, Markus Heinonen, 2015, AISTATS.

SIAM Dashboard for an Industrial Manufacturing Company

Markus Heinonen, 2018 .

Learning with multiple pairwise kernels for drug bioactivity prediction

Juho Rousu, Anna Cichonska, Tapio Pahikkala, 2018, Bioinform..

Metabolite Identification through Machine Learning — Tackling CASMI Challenge Using FingerID

Juho Rousu, Markus Heinonen, Nicola Zamboni, 2013, Metabolites.

ODE2VAE: Deep generative second order ODEs with Bayesian neural networks

Markus Heinonen, Harri Lähdesmäki, Cagatay Yildiz, 2019, NeurIPS.

Deep convolutional Gaussian processes pdf

Samuel Kaski, Markus Heinonen, Kenneth Blomqvist, 2018, ECML/PKDD.

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

Markus Heinonen, Harri Lähdesmäki, Valerii Iakovlev, 2020, ICLR.

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

Maurizio Filippone, Edwin V. Bonilla, Markus Heinonen, 2020, AISTATS.

Bayesian metabolic flux analysis reveals intracellular flux couplings

Juho Rousu, Samuel Kaski, Janne Wallenius, 2018, Bioinform..

CE-merkinnän edellytykset koneenrakennuksessa

Markus Heinonen, 2016 .

Flex ddG: Rosetta ensemble-based estimation of changes in protein-protein binding affinity upon mutation

Markus Heinonen, Tanja Kortemme, Shane Ó Conchúir, 2017, bioRxiv.

Sample-efficient reinforcement learning using deep Gaussian processes pdf

Samuel Kaski, Markus Heinonen, Harri Lähdesmäki, 2020, ArXiv.

Ab Initio Prediction of Molecular Fragments from Tandem Mass Spectrometry Data

Juho Rousu, Taneli Mielikäinen, Markus Heinonen, 2006, German Conference on Bioinformatics.

Learning unknown ODE models with Gaussian processes

Markus Heinonen, Henrik Mannerström, Jukka Intosalmi, 2018, ICML.

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

Markus Heinonen, Harri Lähdesmäki, Çagatay Yildiz, 2019, NeurIPS.

Variational multiple shooting for Bayesian ODEs with Gaussian processes

Samuel Kaski, Markus Heinonen, Pashupati Hegde, 2021 .

Continuous-Time Model-Based Reinforcement Learning

Markus Heinonen, Harri Lähdesmäki, Çagatay Yildiz, 2021, ICML.

De-randomizing MCMC dynamics with the diffusion Stein operator pdf

Samuel Kaski, Markus Heinonen, Zheyang Shen, 2021, NeurIPS.

Affine Transport for Sim-to-Real Domain Adaptation pdf

Samuel Kaski, Ville Kyrki, Markus Heinonen, 2021, ArXiv.

Evolving-Graph Gaussian Processes pdf

Ville Kyrki, Markus Heinonen, David Blanco Mulero, 2021, ArXiv.

Bayesian inference of ODEs with Gaussian processes pdf

Samuel Kaski, Markus Heinonen, Harri Lähdesmäki, 2021, ArXiv.

Flex ddG: Rosetta ensemble-based estimation of changes in protein-protein binding affinity upon mutation

Markus Heinonen, Tanja Kortemme, Pooja Suresh, 2017, bioRxiv.

Genome wide analysis of protein production load in Trichoderma reesei

Markus Heinonen, Mikko Arvas, Merja Penttilä, 2016, Biotechnology for Biofuels.