Efficient Transformers

Transformers has garnered immense interest lately due to their effectiveness across a range of domains like language, vision and reinforcement learning.  The self-attention mechanism is a key defining characteristic of Transformer models. The mechanism can be viewed as a graph-like inductive bias that connects all tokens in a sequence with a relevance-based pooling operation. A […]

MetNet: A Neural Weather Model for Precipitation Forecasting

Weather has an enormous impact on renewable energy and markets, which is expected to reach 80% of the world’s electricity production. There are many social and economic benefits of accurate weather forecasting, from improvements in our daily lives to substantial impacts on agriculture, energy and transportation and to the prevention of human and economic losses […]

Universal Transformers: The Infinite Use of Finite Means!

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 […]