Wavelet-domain hidden Markov models have proven to be useful tools for statistical signal and image processing. The hidden Markov tree (HMT) model captures the key features of the joint statistics of the wavelet coeÆcients of real-world data. One potential drawback to the HMT framework is the need for computationally expensive iterative training (using the EM algorithm, for example). In this pa...

Evaluate the relevance score of text components. Approaches using propagation view the logical structure of a structured document as a tree whose nodes are components of the document and whose edges represent the relationships between the connected nodes. A document component can be either a leaf or an inner node. Leaf nodes are document components that correspond to the last elements of hierar...

We develop a low complexity non-parametric method of nonlinear prediction based on adaptive partitioning of the phase space associated with an observed process. The partitioning method is implemented with a fast recursive algorithm which successively reenes the partition by dyadic splitting. The algorithm discovers the best partition using a Pearson Chi-square test which is equivalent to maximi...

Current solutions to multimedia modeling tasks feature sequential models and static tree-structured models. Sequential models, especially models based on Bidirectional LSTM (BLSTM) and Multilayer LSTM networks, have been widely applied on video, sound, music and text corpora. Despite their success in achieving state-of-the-art results on several multimedia processing tasks, sequential models al...

Gaussian process regression can be accelerated by constructing a small pseudodataset to summarize the observed data. This idea sits at the heart of many approximation schemes, but such an approach requires the number of pseudo-datapoints to be scaled with the range of the input space if the accuracy of the approximation is to be maintained. This presents problems in time-series settings or in s...