6 citations · 9 across the 8 of their papers we have counts for
3 papers · 1 filter
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…
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…