An exploration into the sparse representation of spectra by Mthembu, Linda

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An overview of sparse convex optimization

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Sparse coding for speech recognition

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Discriminant analysis using sparse graphical models by Botha, Dylon

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Antenna elements for sparse-regular aperture arrays by Klopper, Brandt

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Sparse graph codes on a multi-dimensional WCDMA platform

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Sparse subspace clustering-based motion segmentation with complete occlusion handling

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Sub-sampled exponential analysis applied to sparse planar antenna array configurations by Raal, Ridalise

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Strategies to design a cost-effective hub network for sparse air travel demand in Africa

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Solving sparse-reward problems in partially observable 3D environments using distributed reinforcement learning by Louw, Jacobus Martin

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Cooperative multi-agent reinforcement learning in sparse-reward, partially observable 3d environments with curriculum-transfer learning by Denkema, De Wet

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Are solar home systems a more financially viable method of electrifying Ghana households? by Radebe, Thandwefika

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Statistical classification procedures for analyisng functional data by Orsmond, Chane

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Fountain codes and their typical application in wireless standards like edge

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A spin on compressive sensing imaging : reticle-based single-pixel imaging system

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Automatic sub-word unit discovery and pronunciation lexicon induction for automatic speech recognition with application to under-resourced languages by Agenbag, Wiehan

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Localisation under Large Appearance Change by Church, Matthew

“…The network trained learns a mapping from the two parent experiences creating intermediate domains that represent times between the parents, effectively filling in the gaps left by sparse data collection. While the performance of the generation network narrows the functional scope of the system, within that narrow scope, experiences generated from recorded outings outperform the recorded counterparts provided the parent does as well, such that an experience generated from a recording collected at 10:00 and made to mimic the conditions at 14:00 will outperform the recording collected at 14:00. …”
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