Représentations de phrases interprétables avec autoencodeurs variationnels et attention
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A7JNFEA5W" target="_blank" >RIV/00216208:11320/23:7JNFEA5W - isvavai.cz</a>
Result on the web
<a href="https://theses.hal.science/tel-04126269/" target="_blank" >https://theses.hal.science/tel-04126269/</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Représentations de phrases interprétables avec autoencodeurs variationnels et attention
Original language description
"In this thesis, we develop methods to enhance the interpretability of recent representation learning techniques in natural language processing (NLP) while accounting for the unavailability of annotated data. We choose to leverage Variational Autoencoders (VAEs) due to their efficiency in relating observations to latent generative factors and their effectiveness in data-efficient learning and interpretable representation learning."
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů