SDT: Funkciju formēšana dziļajā mašīnmācīšanā. Cover

Funkciju formēšana dziļajā mašīnmācīšanā

Promocijas darba kopsavilkums

Ēvalds Urtāns, Riga Technical University, Latvia

ORCID iD https://orcid.org/0000-0001-9813-0548

This work describes the importance of loss functions and related methods for deep reinforcement learning and deep metric learning. A novel MDQN loss function outperformed DDQN loss function in PLE computer game environments, and a novel Exponential Triplet loss function outperformed the Triplet loss function in the face re-identification task with VGGFace2 dataset reaching 85,7 % accuracy using zero-shot setting. This work also presents a novel UNet-RNN-Skip model to improve the performance of the value function for path planning tasks.

Additional information

Publication type

DOI

https://doi.org/10.7250/9789934226847

Defence date

01.12.2021.

Format

ISBN (pdf)

978-9934-22-684-7

Pages

76

Published online

Publication language

Publisher

RTU Press

Country of Publication

Latvia

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