29 citations · 37 across the 6 of their papers we have counts for
6 papers
CFMI: Flow Matching for Missing Data Imputation
Vaidotas Simkus, Michael U. Gutmann
We introduce conditional flow matching for imputation (CFMI), a new general-purpose method to impute missing data. The method combines continuous normalising flows, flow-matching,…
Orb-v3: atomistic simulation at scale
Benjamin Rhodes, Sander Vandenhaute, Vaidotas Šimkus +4
We introduce Orb-v3, the next generation of the Orb family of universal interatomic potentials. Models in this family expand the performance-speed-memory Pareto frontier, offering…
Improving Variational Autoencoder Estimation from Incomplete Data with Mixture Variational Families
Vaidotas Simkus, Michael U. Gutmann
We consider the task of estimating variational autoencoders (VAEs) when the training data is incomplete. We show that missing data increases the complexity of the model's posterior…
Conditional Sampling of Variational Autoencoders via Iterated Approximate Ancestral Sampling
Vaidotas Simkus, Michael U. Gutmann
Conditional sampling of variational autoencoders (VAEs) is needed in various applications, such as missing data imputation, but is computationally intractable. A principled choice…
Learning Job Titles Similarity from Noisy Skill Labels
Rabih Zbib, Lucas Alvarez Lacasa, Federico Retyk +5
Measuring semantic similarity between job titles is an essential functionality for automatic job recommendations. This task is usually approached using supervised learning techniqu…
Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data
Vaidotas Simkus, Benjamin Rhodes, Michael U. Gutmann
Statistical models are central to machine learning with broad applicability across a range of downstream tasks. The models are controlled by free parameters that are typically esti…