My name is Arthur Bražinskas (pronounced Bra [zh] inskas), I’m a natural language processing Ph.D. researcher working on latent probabilistic models for abstractive opinion summarization. I’m part of the ILCC group at the University of Edinburgh and supervised by Ivan Titov and Mirella Lapata. Specifically, I focus on low-resource settings where annotated datasets are scarce yet large amounts of unannotated data are available. In these settings, the model learns the process of summarization without direct supervision or from a few examples.
I'm interested in Bayesian machine learning approaches that model data in terms of random variables - observable and hidden. These models have solid foundation in information theory and more recently have been fueled by neural networks. For training, my preference is amortized variational inference (i.e., VAE) combined with reinforcement learning.
I graduated (MSc. \w distinction) in artificial intelligence from the University of Amsterdam, Netherlands, where I specialized in theoretical machine learning and natural language processing. Before starting my Ph.D., I worked on machine learning modeling at Elsevier, Amazon, and Zalando.