Probabilistic programs in high-dimensional domains


Probabilistic programs in high-dimensional domains – We present a principled framework for learning a probabilistic programming language by probabilistic programming. This framework takes as input a probabilistic programming grammar and learns a parser from this grammar. These grammar parsers are known to be good at probabilistic programming, and we propose a language for learning parsers that can learn parsers from probabilistic programs. We show that this parsers can be trained efficiently on synthetic and real data, and the framework is robust to the constraints imposed by the real data. We also report our analysis of the learning task of the parser for probabilistic programs.

In recent years many applications in computer vision have focussed on the problem of human-computer interactions (HCI). However, the HCI approach is far from a complete solution, as its basic objective is to solve a large HCI problem. Our goal is, instead, to improve the HCI approach by exploiting the HCI-based representations of input representations. In this work we present a novel CNN-based framework for solving HCI. This framework is very flexible and can be used for any HCI dataset. In particular, it combines the well-known RNN network structure and nonnegative matrix factorization in a fully connected framework. The model-based framework is then used as a first step towards achieving a state-of-the-art HCI model. Experiments on two benchmark datasets, namely the COCO-2012 and the COCO-16 datasets, show that our framework provides improved results compared to state of the art approaches. We believe this work should not only assist HCI researchers in solving the HCI system, but also further enhance the HCI framework.

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Probabilistic programs in high-dimensional domains

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    Facial Recognition based on the Bayes-type Feature SpaceIn recent years many applications in computer vision have focussed on the problem of human-computer interactions (HCI). However, the HCI approach is far from a complete solution, as its basic objective is to solve a large HCI problem. Our goal is, instead, to improve the HCI approach by exploiting the HCI-based representations of input representations. In this work we present a novel CNN-based framework for solving HCI. This framework is very flexible and can be used for any HCI dataset. In particular, it combines the well-known RNN network structure and nonnegative matrix factorization in a fully connected framework. The model-based framework is then used as a first step towards achieving a state-of-the-art HCI model. Experiments on two benchmark datasets, namely the COCO-2012 and the COCO-16 datasets, show that our framework provides improved results compared to state of the art approaches. We believe this work should not only assist HCI researchers in solving the HCI system, but also further enhance the HCI framework.


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