This paper concerns learning and prediction with probabilistic models where the domain sizes of latent variables have no a priori upper-bound. Current approaches represent prior d...
: Network topology not only tells about tightly-connected “communities,” but also gives cues on more subtle properties of the vertices. We introduce a simple probabilistic late...
We develop nonparametric Bayesian models for multiscale representations of images depicting natural scene categories. Individual features or wavelet coefficients are marginally de...
Jyri J. Kivinen, Erik B. Sudderth, Michael I. Jord...
The classical probabilistic models attempt to capture the Ad hoc information retrieval problem within a rigorous probabilistic framework. It has long been recognized that the prim...
This paper presents a Bayesian method for temporally aligning a music score and an audio rendition. A critical problem in audio-toscore alignment is in dealing with the wide varie...
Akira Maezawa, Hiroshi G. Okuno, Tetsuya Ogata, Ma...