Lecture

Contrastive losses: Word2Vec and Skip-gram

Description

This lecture introduces contrastive losses for representation learning, focusing on Word2Vec and Skip-gram models. It covers the training objectives, context windows, negative sampling, and Noise Contrastive Estimation. The lecture also explores InfoNCE/CPC and its applications in image and graph data, as well as related topics like Deep Metric Learning and Energy-based models.

Official source

https://mediaspace.epfl.ch/media/0_o5fkzoxs

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Ontological neighbourhood

Information engineering

Machine learning: Artificial neural networks

Mathematics

Algebra: Representation theory

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