Ruprecht-Karls-Universität Heidelberg
Institut für Computerlinguistik

Bilder vom Neuenheimer Feld, Heidelberg und der Universität Heidelberg

Invariant Learning and Disentanglement for NLP

Kursbeschreibung

Studiengang Modulkürzel Leistungs-
bewertung
BA-2010 AS-CL 8 LP
Master SS-CL, SS-TAC 8 LP
Dozenten/-innen Artem Sokolov
Veranstaltungsart Hauptseminar (Block)
Sprache English
Erster Termin 10.10.2022
Letzter Termin 14.10.2022
Zeit und Ort täglich, 10:00-17:00, INF 326 / SR 27
Commitment-Frist tbd.

Teilnahmevoraussetzungen

Knowledge of the following will be helpful: foundations of probability and statistics, machine learning, natural language processing and neural networks.

Leistungsnachweis

  • Regular and active participation
  • Paper presentation
  • Implementation project in small groups (during the semester, TBD)

Inhalt

This module is an introduction to the emerging subfield of machine learning of invariance-based learning and disentanglement, which tries to uncover hidden true causal factors that govern natural data, to introduce better inductive biases for models or to increase robustness to the out-of-distribution inputs. Assuming a close approximation to the factors has been uncovered, a fine-grained control of the neural networks outputs becomes possible, which are also more robust to the presence of spurious input features. Most of the research has been done so far in computer vision, however, we will also include papers that attempt applications to natural language processing.

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