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Exotic latent spaces for sequence autoencoders
Organisatsiooni nimiWise Plc
KokkuvõteExotic latent spaces for sequence autoencoders
Variational Autoencoders are a staple of neural network architectures, useful for distilling the essential characteristics of the input data into a constant-dimension 'embedding' in the latent space. The 'Variational' part refers to the variational approximation of the posterior distribution in the latent space, when interpreted in a Bayesian fashion. This thesis will look at autoencoders of variable-length sequences (such as sequences of transactions at an online service) and compare the performance of the variational approximation to at least one of two other latent space approaches: firstly, an Information Bottleneck constraint, which does away with the crude Gaussian prior assumption of the VAE, and secondly, a hyperbolic latent space geometry, which has been claimed to better represent the degree of uncertainty in the model's predictions. Starting point will be the sequence VAE as implemented in (which includes a draft implementation of Information Bottleneck). A reasonably high level of math knowledge is a prerequisite.,
Lõputöö kaitsmise aasta2022-2023
JuhendajaEgor Kraev (Wise), Prof.Meelis Kull
Suhtlemiskeel(ed)inglise keel
Nõuded kandideerijale
Tase Magister
Kandideerimise kontakt
Nimi Egor Kraev