Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 8 of 8 for “"sparse features"”.
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Statistical methods for learning sparse features
… it is appealing if we can extract the hidden sparse structure of the data since sparse structures allow us to understand and interpret the information better. The aim of this thesis is to develop algorithms that can extract such hidden sparse structures of the data in the context of both …
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Learning sparse features and metric in signal and image processing
… in computational neuroscience in the context of sparse coding in the visual system, sparse coding plays a key role in feature representation learning, as the over-complete dictionary allows more representation flexibility and efficiency, and captures structures and patterns inherent in the raw …
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Improving Large-scale Recommendation Systems with Contextual Signals
… propose: (i) a new linear embedding method for sparse features, thus, allowing us to quickly convert sparse features to latent vector space for further training while still preserving the comparable embedding quality to neural network embedding models. (ii) a new offline embedding refinement …
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Learning environment simulators from sparse signals
… this work instead seeks to learn from much sparser signals, like the agent's reward. In Chapter 1, we establish a taxonomy of environments and the attributes that make them easier or harder to model through learning. In Chapter 2, we review prior work in the field of environment learning. In …
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A biologically inspired system for action recognition
… system architectures. Besides, we find that sparse features in intermediate stages outperform dense ones and that using a simple feature selection approach leads to an efficient system that performs better with far fewer features. We test the approach on different publicly available action …
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Challenges in recommender systems : scalability, privacy, and structured recommendations
… generation method that explicitly maintains a sparse dual and the corresponding low rank primal solution. We provide a new dual block coordinate descent algorithm for solving the dual problem with a few spectral constraints. Empirical results illustrate the effectiveness of our method in …
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Signal sampling and processing in magnetic resonance applications
… which are a priori known to be composed of sparse features. $L_1$ regularization is shown to be stable at signal-to-noise ratios < 20 and capable of resolving relaxation time constants and diffusion coefficients which differ by as little as 10%, such as in relaxation and diffusion studies of …