SIGIR2018 Workshop on Learning From Noisy/Limited Data for IR

We are organizing the “Learning From Noisy/Limited Data for Information Retrieval” workshop which is co-located with SIGIR 2018. This is the first edition of this workshop and The goal of the workshop is to bring together researchers from industry, where data is plentiful but noisy, with researchers from academia, where data is sparse but clean, to […]

Fidelity-Weighted Learning

Our paper “Fidelity-Weighted Learning”, with Arash Mehrjou, Stephan Gouws, Jaap Kamps, Bernhard Schölkopf, has been accepted at Sixth International Conference on Learning Representations (ICLR2018). \o/ [perfectpullquote align=”full” bordertop=”false” cite=”” link=”” color=”” class=”#16989D” size=”16″] tl;dr Fidelity-weighted learning (FWL) is a semi-supervised student-teacher approach for training deep neural networks using weakly-labeled data. It modulates the parameter updates to […]