We use a statistical method to select the most probable structure or parse for a given sentence. It takes as input the dependency structures generated for the sentence by a depend...
In recent years there is much interest in word cooccurrence relations, such as n-grams, verb-object combinations, or cooccurrence within a limited context. This paper discusses ho...
The principle of maximizing mutual information is applied to learning overcomplete and recurrent representations. The underlying model consists of a network of input units driving...
We discuss an idea for collecting data in a relatively efficient manner. Our point of view is Bayesian and information-theoretic: on any given trial, we want to adaptively choose...
The maximisation of information transmission over noisy channels is a common, albeit generally computationally difficult problem. We approach the difficulty of computing the mutua...
Mutual information is widely used, in a descriptive way, to measure the stochastic dependence of categorical random variables. In order to address questions such as the reliabilit...
Mutual information may be used to select the embedding lag of a time series. However, this lag selection is usually limited to the analysis of the mutual information between a pair...
: This work is on development of a method for automatic registration of satellite images acquired on different dates, for both geometric and radiometric correction with respect to ...
In this paper we introduce the MeanNN approach for estimation of main information theoretic measures such as differential entropy, mutual information and divergence. As opposed to...