| Multiple importance sampling elbo and deep ensembles of variational approximations O Kviman, H Melin, H Koptagel, V Elvira, J Lagergren International Conference on Artificial Intelligence and Statistics, 10687-10702, 2022 | 15 | 2022 |
| Cooperation in the latent space: The benefits of adding mixture components in variational autoencoders O Kviman, R Molén, A Hotti, S Kurt, V Elvira, J Lagergren International Conference on Machine Learning, 18008-18022, 2023 | 8* | 2023 |
| Vaiphy: a variational inference based algorithm for phylogeny H Koptagel, O Kviman, H Melin, N Safinianaini, J Lagergren Advances in Neural Information Processing Systems 35, 14758-14770, 2022 | 8 | 2022 |
| Variational Resampling O Kviman, V Branchini, N Elvira, J Lagergren International Conference on Artificial Intelligence and Statistics, 3286-3294, 2024 | 1 | 2024 |
| Improved Variational Bayesian Phylogenetic Inference using Mixtures O Kviman, R Molén, J Lagergren arXiv preprint arXiv:2310.00941, 2023 | 1 | 2023 |
| Sequence Disambiguation with Synaptic Traces in Associative Neural Networks RH Martinez, O Kviman, A Lansner, P Herman Artificial Neural Networks and Machine Learning–ICANN 2019: Theoretical …, 2019 | 1 | 2019 |
| Indirectly Parameterized Concrete Autoencoders A Nilsson, K Wijk, E Englesson, A Hotti, C Saccardi, O Kviman, ... arXiv preprint arXiv:2403.00563, 2024 | | 2024 |
| Statistical Distance Based Deterministic Offspring Selection in SMC Methods O Kviman, H Koptagel, H Melin, J Lagergren arXiv preprint arXiv:2212.12290, 2022 | | 2022 |
| KL/TV Reshuffling: Statistical Distance Based Offspring Selection in SMC Methods O Kviman | | 2022 |
| [Re] Tensor Monte Carlo: Particle Methods for the GPU Era O Kviman, L Nilsson, M Larsson | | 2019 |
| Applicability of a Translucent Barrier Based Model of Noise O Kviman, L Nilsson | | 2018 |
Jens LagergrenKTH, Royal Institute of TechnologyVerified email at scilifelab.se
