Universal Transformers

Thanks to Stephan Gouws for his help on writing and improving this blog post. Transformers have recently become a competitive alternative to RNNs for a range of sequence modeling tasks. They address a significant shortcoming of RNNs, i.e. their inherently sequential computation which prevents parallelization across elements of the input sequence, whilst still addressing the […]

Learning to Transform, Combine, and Reason in Open-Domain Question Answering

Our paper “Learning to Transform, Combine, and Reason in Open-Domain Question Answering”, with Hosein Azarbonyad, Jaap Kamps, and Maarten de Rijke, has been accepted as a long paper at 12th ACM International Conference on Web Search and Data Mining (WSDM 2019).\o/ We have all come to expect getting direct answers to complex questions from search […]

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 […]