Yandex School of Data Analysis Conference
Machine Learning: Prospects and Applications
https://yandexdataschool.com/conference
The classic paradigm of predictive modelling in supervised machine learning
involves training classifiers or regressors to predict a target variable
of interest based on large quantities of annotated training data. However,
despite the age of “big data”, it is often the case that the specific category
of interest to be recognised has few or no prior examples — as in the
case of rare or recently emerged phenomena. This talk will introduce the
new and exciting area of zero-shot machine learning, which addresses this
setting of supervised prediction with zero prior training examples.
I will show how zero-shot learning can be achieved with a few strategies
including via semantic attributes and distributed (vector space) models of
words. We will see how zero-shot learning can be understood from a variety
of perspectives including as an extreme form of classifier or regressor
generalisation, learning a cross-modal embedding, or as a particular
category of neural network.
Throughout the talk, I will introduce a variety of contemporary example
applications of zero-shot learning including computer vision, forensics,
and natural language processing. Finally, I will outline a variety of current
research issues and open questions in zero-shot learning including unification
of attribute and vector space approaches, transductive learning,
and zero-shot domain adaptation.
