Stochastic Nonparametric Learning via Sparse Coding


Stochastic Nonparametric Learning via Sparse Coding – Our goal is to learn continuous representations of input vectors with a novel nonparametric representation, in particular, the sparsity-inducing distribution of the sparse coefficients. By using a regularized kernel model as the input, we show that a sparsity-inducing distribution can significantly improve the performance of our method. We also demonstrate that both distributions are effectively trained on images with dense residual images (via a novel sparse coding model).

This paper presents a novel framework for learning a Bayesian inference graph from a dataset of real world data using a Bayesian model. Such a Bayesian model has the following properties: it can be learned efficiently in an incremental manner, and thus it can be used to explore new Bayesian inference procedures without relying on the standard data-driven approach. Our approach exploits prior knowledge about the underlying data to design its Bayesian inference procedure. We also show that the proposed approach can be used for learning from data in other than the data.

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Stochastic Nonparametric Learning via Sparse Coding

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  • Stacked Generative Adversarial Networks for Multi-Resolution 3D Point Clouds Regression

    Bayesian Inference via Variational Matrix FactorizationThis paper presents a novel framework for learning a Bayesian inference graph from a dataset of real world data using a Bayesian model. Such a Bayesian model has the following properties: it can be learned efficiently in an incremental manner, and thus it can be used to explore new Bayesian inference procedures without relying on the standard data-driven approach. Our approach exploits prior knowledge about the underlying data to design its Bayesian inference procedure. We also show that the proposed approach can be used for learning from data in other than the data.


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