We investigate the problem of learning a widely-used latent-variable model – the Latent Dirichlet Allocation (LDA) or “topic” model – using distributed computation, where ...
David Newman, Arthur Asuncion, Padhraic Smyth, Max...
In this paper, we propose a robust incremental learning framework for accurate skin region segmentation in real-life images. The proposed framework is able to automatically learn ...
Abstract—We propose a probabilistic model for analyzing spatial activation patterns in multiple functional magnetic resonance imaging (fMRI) activation images such as repeated ob...
— This paper investigates spatiotemporal feature extraction from temporal image sequences based on invariance representation. Invariance representation is one of important functi...
We present a critique of language-based modelling for text input research, and propose an alternative inputbased approach. Current language-based statistical models are derived fr...