3 papers
cs.LG2026
Multi-Marginal Flow Matching with Adversarially Learnt Interpolants
Oskar Kviman, Kirill Tamogashev, Nicola Branchini +3
Learning the dynamics of a process given sampled observations at several time points is an important but difficult task in many scientific applications. When no ground-truth trajec…
cs.LG2024
Indirectly Parameterized Concrete Autoencoders
Alfred Nilsson, Klas Wijk, Sai bharath chandra Gutha +7
Feature selection is a crucial task in settings where data is high-dimensional or acquiring the full set of features is costly. Recent developments in neural network-based embedded…
cs.LG2024
Efficient Mixture Learning in Black-Box Variational Inference
Alexandra Hotti, Oskar Kviman, Ricky Molén +2
Mixture variational distributions in black box variational inference (BBVI) have demonstrated impressive results in challenging density estimation tasks. However, currently scaling…