Abstract--A crucial issue in designing learning machines is to select the correct model parameters. When the number of available samples is small, theoretical sample-based generali...
An approach for fast tracking of arbitrary image features with no prior model and no offline learning stage is presented. Fast tracking is achieved using banks of linear displacem...
Liam Ellis, Nicholas Dowson, Jiri Matas, Richard B...
In this paper, we propose a probabilistic kernel approach to preference learning based on Gaussian processes. A new likelihood function is proposed to capture the preference relat...
In this analysis of profiles and messaging behavior on a major online dating service, we find that, consistent with predictions of evolutionary psychology, women as compared to me...
Andrew T. Fiore, Lindsay Shaw Taylor, Xiaomeng Zho...
We present a post-processing technique that selectively reduces the salience of distracting regions in an image. Computational models of attention predict that texture variation i...