Visual recognition is a computational challenge that is thought to occur via efficient coding. An important concept is sparseness, a measure of coding efficiency. The prevailing view is that sparseness supports efficiency by minimizing redundancy and correlations in spiking populations. Yet, we recently reported that "choristers", neurons that behave more similarly (have correlated stimulus pre...

Non-negative Matrix Factorization (NMF), especially with sparseness constraints, plays a critically important role in data engineering and machine learning. Hoyer (2004) presented an algorithm to compute NMF with exact sparseness constraints. The exact sparseness constraints depends on a projection operator. In the present work, we first give a very simple counterexample, for which the projecti...

this study set out to investigate the distribution of the syntactic properties associatedwith the prodrop parameter by persian-speaking l2 learners of english in an attemptto examine the (un)attainability of native-like knowledge and (in)accessibility of ugin adult sla. to do so, 92 participants were assigned into different levels of l2knowledge based on their performances on the oxford placeme...

When translating into morphologically rich languages, Statistical MT approaches face the problem of data sparsity. The severity of the sparseness problem will be high when the corpus size of morphologically richer language is less. Even though we can use factored models to correctly generate morphological forms of words, the problem of data sparseness limits their performance. In this paper, we...

Radon transforms are popular operators for velocity analysis (Taner and Koehler, 1969; Guitton and Symes, 2003), noise attenuation (Foster and Mosher, 1992), and data interpolation (Hindriks and Duijndam, 1998; Trad et al., 2002). One property that is often sought in radon domains is sparseness, where the energy in the model space is well focused for each corresponding event in the data space. ...

The proposal that cortical activity in the visual cortex is optimized for sparse neural activity is one of the most established ideas in computational neuroscience. However, direct experimental evidence for optimal sparse coding remains inconclusive, mostly due to the lack of reference values on which to judge the measured sparseness. Here we analyze neural responses to natural movies in the pr...