Title: Knowledge Transfer Using Latent Variable Models
Speaker: Ayan Acharya, University of Texas, Austin
Abstract:
In several applications, scarcity of labeled data is a challenging
problem that hinders the predictive capabilities of machine learning
algorithms. Additionally, the distribution of the data changes over time
rendering models trained with older data less capable of discovering
useful structure from the newly available data. Transfer learning is a
convenient framework to overcome such problems where the learning of a
model specific to a domain can benefit the learning of other models in
other domains through either simultaneous training of domains or
systematic sequential transfer of knowledge from one domain to the
others.
In this talk, variety of methods for achieving transfer learning are
explored where a hidden layer is shared across different domains in
hierarchical latent variable based models. Applications of such
frameworks in problems like text classification, object recognition from
images, network modeling for community detection and evolution of count
values vectors and matrices have shown excellent results so far. In
particular, the strength of non-parametric Poisson factorization will
also be explored in the context of knowledge transfer and modeling of
large scale count data prevalent in text mining, image analysis and
recommender systems.