99 citations · 347 across the 39 of their papers we have counts for
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Modular Flows: Differential Molecular Generation
Yogesh Verma, Samuel Kaski, Markus Heinonen +1
Generating new molecules is fundamental to advancing critical applications such as drug discovery and material synthesis. Flows can generate molecules effectively by inverting the…
Latent Neural ODEs with Sparse Bayesian Multiple Shooting
Valerii Iakovlev, Cagatay Yildiz, Markus Heinonen +1
Training dynamic models, such as neural ODEs, on long trajectories is a hard problem that requires using various tricks, such as trajectory splitting, to make model training work i…
Incorporating functional summary information in Bayesian neural networks using a Dirichlet process likelihood approach
Vishnu Raj, Tianyu Cui, Markus Heinonen +1
Bayesian neural networks (BNNs) can account for both aleatoric and epistemic uncertainty. However, in BNNs the priors are often specified over the weights which rarely reflects tru…
Tackling covariate shift with node-based Bayesian neural networks
Trung Trinh, Markus Heinonen, Luigi Acerbi +1
Bayesian neural networks (BNNs) promise improved generalization under covariate shift by providing principled probabilistic representations of epistemic uncertainty. However, weigh…
Generative Modelling With Inverse Heat Dissipation
Severi Rissanen, Markus Heinonen, Arno Solin
While diffusion models have shown great success in image generation, their noise-inverting generative process does not explicitly consider the structure of images, such as their in…