| Variational Sequential Monte Carlo CA Naesseth, SW Linderman, R Ranganath, DM Blei The 21st International Conference on Artificial Intelligence and Statistics …, 2018 | 267 | 2018 |
| Reparameterization gradients through acceptance-rejection sampling algorithms CA Naesseth, FJR Ruiz, SW Linderman, DM Blei The 20th International Conference on Artificial Intelligence and Statistics …, 2017 | 136 | 2017 |
| Elements of Sequential Monte Carlo CA Naesseth, F Lindsten, TB Schön Foundations and Trends® in Machine Learning 12 (3), 307-392, 2019 | 115 | 2019 |
| Sequential Monte Carlo Methods for System Identification TB Schön, F Lindsten, J Dahlin, J Wågberg, CA Naesseth, A Svensson, ... IFAC Symposium on System Identification, 2015 | 109 | 2015 |
| Nested Sequential Monte Carlo Methods CA Naesseth, F Lindsten, TB Schön The 32nd International Conference on Machine Learning (ICML) 37, 1292–1301, 2015 | 92 | 2015 |
| Divide-and-conquer with sequential Monte Carlo F Lindsten, AM Johansen, CA Naesseth, B Kirkpatrick, TB Schön, ... Journal of Computational and Graphical Statistics 26 (2), 445-458, 2017 | 74 | 2017 |
| Markovian Score Climbing: Variational Inference with KL(p||q) CA Naesseth, F Lindsten, D Blei Advances in Neural Information Processing Systems 34, 2020 | 62 | 2020 |
| Practical and asymptotically exact conditional sampling in diffusion models L Wu, B Trippe, CA Naesseth, D Blei, JP Cunningham Advances in Neural Information Processing Systems 36, 2023 | 59 | 2023 |
| Sequential Monte Carlo for Graphical Models CA Naesseth, F Lindsten, TB Schön Advances in Neural Information Processing Systems 27, 2014 | 52 | 2014 |
| High-dimensional filtering using nested sequential Monte Carlo CA Naesseth, F Lindsten, TB Schön IEEE Transactions on Signal Processing 67 (16), 4177-4188, 2019 | 46 | 2019 |
| Interacting Particle Markov Chain Monte Carlo T Rainforth, CA Naesseth, F Lindsten, B Paige, JW van de Meent, ... The 33rd International Conference on Machine Learning (ICML) 48, 2616–2625, 2016 | 43 | 2016 |
| Twisted Variational Sequential Monte Carlo D Lawson, G Tucker, CA Naesseth, CJ Maddison, RP Adams, YW Teh 3rd workshop on Bayesian Deep Learning (NeurIPS), 2018 | 24 | 2018 |
| Variational Combinatorial Sequential Monte Carlo Methods for Bayesian Phylogenetic Inference AK Moretti, L Zhang, CA Naesseth, H Venner, D Blei, I Pe'er The 37th Conference on Uncertainty in Artificial Intelligence (UAI), 2021 | 22 | 2021 |
| A Variational Perspective on Generative Flow Networks H Zimmermann, F Lindsten, JW van de Meent, CA Naesseth Transactions on Machine Learning Research, 2023 | 21 | 2023 |
| E-valuating classifier two-sample tests T Pandeva, T Bakker, CA Naesseth, P Forré Transactions on Machine Learning Research, 2024 | 12 | 2024 |
| Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transport L Zhang, DM Blei, CA Naesseth Transactions on Machine Learning Research, 2023 | 10 | 2023 |
| Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion Modelling G Bartosh, D Vetrov, CA Naesseth Advances in Neural Information Processing Systems 37, 2024 | 7 | 2024 |
| Variational Flow Matching for Graph Generation F Eijkelboom, G Bartosh, CA Naesseth, M Welling, JW van de Meent Advances in Neural Information Processing Systems 37, 2024 | 6 | 2024 |
| Capacity estimation of two-dimensional channels using Sequential Monte Carlo CA Naesseth, F Lindsten, TB Schön The 2014 IEEE Information Theory Workshop, 2014 | 6 | 2014 |
| Fast yet Safe: Early-Exiting with Risk Control M Jazbec, A Timans, TH Veljković, K Sakmann, D Zhang, CA Naesseth, ... Advances in Neural Information Processing Systems 37, 2024 | 4 | 2024 |
Fredrik LindstenAssociate Professor, Linköping University確認したメール アドレス: liu.se