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Organisatsiooni nimi

Geometric Deep Learning

Kokkuvõte

Transformer architectures give outstanding results on many NLP tasks, particularly sequential data. The advance of transformer models over LSTM is that they predict long-distance dependencies directly, rather than passing long-distance dependencies down a long recurrence chain. The Transformer models have broken long-distance dependencies from the recurrence mechanism using a direct matrix product. The multi-layers are building hierarchies over what they are "attending" to. We propose new transformer architecture that has an exploits hierarchy implicitly and explicitly. Our application areas of large-scale machine learning models are

Lõputöö kaitsmise aasta

2022-2023

Juhendaja

Kallol Roy

Suhtlemiskeel(ed)

inglise keel

Nõuded kandideerijale

Tase

Bakalaureus, Magister

Märksõnad

#Transformers, Group Theory

 

Nimi

KALLOL ROY

Tel

+37256051480

E-mail

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