Learning to Rank for Word Detection


Learning to Rank for Word Detection – The aim of this paper is to train a classifier that is more sensitive to the content of the input (e.g. content of text) and less sensitive (e.g. content size). We propose two sub-modular Bayesian models, the first using the concept of an unbiased model and the second using the concept of an unbiased model both as a surrogate model. Experiments on CIFAR-10 and MSRC 2015 datasets show that the proposed model has significantly improved the accuracy of the Bayesian model and outperforms the state of the art on both tasks.

In this paper, we propose a new spectral learning method for supervised learning of image features that is a very promising extension of convolutional neural networks (CNN) and its recent approaches. Our method is much faster to implement than existing spectral-learning methods, but does not require any preprocessing of the data for the training and validation. We first improve the training procedure via training samples from different channels by using two different techniques: (i) a novel deep Gaussian network (DGGN) which directly learns latent features from the data, (ii) a stochastic gradient regression (SGR) which combines a Gaussian model into a fully Gaussian model with a non-linear dependence on the input data, while the DGGN takes the input input parameters as input and adapts the gradient of this model to the input parameters. Our method is based on a deep neural network, and directly learns a new model for the data.

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Learning to Rank for Word Detection

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  • Deep Inference Network: Learning Disentangling by Exploiting Deep Architecture

    A New Spectral Feature Selection Method for Robust Object Detection in Unstructured ContextsIn this paper, we propose a new spectral learning method for supervised learning of image features that is a very promising extension of convolutional neural networks (CNN) and its recent approaches. Our method is much faster to implement than existing spectral-learning methods, but does not require any preprocessing of the data for the training and validation. We first improve the training procedure via training samples from different channels by using two different techniques: (i) a novel deep Gaussian network (DGGN) which directly learns latent features from the data, (ii) a stochastic gradient regression (SGR) which combines a Gaussian model into a fully Gaussian model with a non-linear dependence on the input data, while the DGGN takes the input input parameters as input and adapts the gradient of this model to the input parameters. Our method is based on a deep neural network, and directly learns a new model for the data.


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