2 citations · 5 across the 5 of their papers we have counts for
6 papers
VIKING: Deep variational inference with stochastic projections
Samuel G. Fadel, Hrittik Roy, Nicholas Krämer +5
Variational mean field approximations tend to struggle with contemporary overparametrized deep neural networks. Where a Bayesian treatment is usually associated with high-quality p…
Identifying Metric Structures of Deep Latent Variable Models
Stas Syrota, Yevgen Zainchkovskyy, Johnny Xi +2
Deep latent variable models learn condensed representations of data that, hopefully, reflect the inner workings of the studied phenomena. Unfortunately, these latent representation…
A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences
Miguel González-Duque, Richard Michael, Simon Bartels +3
Optimizing discrete black-box functions is key in several domains, e.g. protein engineering and drug design. Due to the lack of gradient information and the need for sample efficie…
A Continuous Relaxation for Discrete Bayesian Optimization
Richard Michael, Simon Bartels, Miguel González-Duque +4
To optimize efficiently over discrete data and with only few available target observations is a challenge in Bayesian optimization. We propose a continuous relaxation of the object…
Probabilistic thermal stability prediction through sparsity promoting transformer representation
Yevgen Zainchkovskyy, Jesper Ferkinghoff-Borg, Anja Bennett +5
Pre-trained protein language models have demonstrated significant applicability in different protein engineering task. A general usage of these pre-trained transformer models laten…
Robust uncertainty estimates with out-of-distribution pseudo-inputs training
Pierre Segonne, Yevgen Zainchkovskyy, Søren Hauberg
Probabilistic models often use neural networks to control their predictive uncertainty. However, when making out-of-distribution (OOD)} predictions, the often-uncontrollable extrap…