Investigations on neural networks, discriminative training criteria and error bounds
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
RWTH Aachen University, Dissertation, 2020
- Keyword
-
Error
Training
- Event
-
Veröffentlichung
- (where)
-
Aachen
- (who)
-
Universitätsbibliothek der RWTH Aachen
- (when)
-
2020
- Creator
- Contributor
- DOI
-
10.18154/RWTH-2020-11914
- URN
-
urn:nbn:de:101:1-2021020208060512812874
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
25.03.2025, 1:44 PM CET
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Nussbaum-Thom, Markus
- Ney, Hermann
- Häb-Umbach, Reinhold
- Universitätsbibliothek der RWTH Aachen
Time of origin
- 2020
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Advancing neural language modeling in automatic speech recognition
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Generating Alignments Using Target Foresight in Attention-Based Neural Machine Translation
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Upper and Lower Tight Error Bounds for Feature Omission with an Extension to Context Reduction
Biasing Attention-Based Recurrent Neural Networks Using External Alignment Information
Context-aware neural machine translation
Alignment models for recurrent neural networks
Neural machine translation for low-resource scenarios
Alignment-based neural networks for machine translation
Advancing neural language modeling in automatic speech recognition
Improving Neural Language Models with Weight Norm Initialization and Regularization
Generating Alignments Using Target Foresight in Attention-Based Neural Machine Translation
Towards Two-Dimensional Sequence to Sequence Model in Neural Machine Translation
On The Alignment Problem in Multi-Head Attention-Based Neural Machine Translation
Investigations on Phrase-based Decoding with Recurrent Neural Network Language and Translation Models
Upper and Lower Tight Error Bounds for Feature Omission with an Extension to Context Reduction
Biasing Attention-Based Recurrent Neural Networks Using External Alignment Information
Context-aware neural machine translation
Alignment models for recurrent neural networks
Neural machine translation for low-resource scenarios
Alignment-based neural networks for machine translation
Advancing neural language modeling in automatic speech recognition
Improving Neural Language Models with Weight Norm Initialization and Regularization
Generating Alignments Using Target Foresight in Attention-Based Neural Machine Translation
Towards Two-Dimensional Sequence to Sequence Model in Neural Machine Translation
On The Alignment Problem in Multi-Head Attention-Based Neural Machine Translation