Imitation learning, zero-shot learning and automated fact checking : Informatisches Kolloquium
- Link:
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https://lecture2go.uni-hamburg.de/l2go/-/get/v/23243
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- Autor/in:
- Beteiligte Personen:
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- Regionales Rechenzentrum der Universität Hamburg/ MCC/ Lecture2Go
- DL.MIN
- Verlag/Körperschaft:
- Universität Hamburg
- Erscheinungsjahr:
- 2018
- Medientyp:
- Audiovisuell
- Schlagwort:
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- Informatik
- Beschreibung:
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- The slides are available as a PDF under the download tab In this talk I will give an overview of my research in machine learning for natural language processing. I will begin by introducing my work on imitation learning, a machine learning paradigm I have used to develop novel algorithms for structure prediction that have been applied successfully to a number of tasks such as semantic parsing, natural language generation and information extraction. Key advantages are the ability to handle large output search spaces and to learn with non-decomposable loss functions. Following this, I will discuss my work on zero-shot learning using neural networks, which enabled us to learn models that can predict labels for which no data was observed during training. I will conclude with my work on automated fact-checking, a challenge we proposed in order to stimulate progress in machine learning, natural language processing and, more broadly, artificial intelligence.
- Lizenz:
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- UHH-L2G
- Quellsystem:
- Lecture2Go UHH
Interne Metadaten
- Quelldatensatz
- oai:lecture2go.uni-hamburg.de:23243