When related learning tasks are naturally arranged in a hierarchy, an appealing approach for coping with scarcity of instances is that of transfer learning using a hierarchical Ba...
Gal Elidan, Benjamin Packer, Geremy Heitz, Daphne ...
Traditional supervised visual learning simply asks annotators “what” label an image should have. We propose an approach for image classification problems requiring subjective...
We present an adaptation technique for statistical machine translation, which applies the well-known Bayesian learning paradigm for adapting the model parameters. Since state-of-t...
Abstract— Many games require opponent modelling for optimal performance. The implicit learning and adaptive nature of evolutionary computation techniques offer a natural way to d...
We propose Action-Reaction Learning as an approach for analyzing and synthesizing human behaviour. This paradigm uncovers causal mappings between past and future events or between...