Matching-Gap Boosting for Sparse Signal Recovery with Nonparametric Noise Distributions


Matching-Gap Boosting for Sparse Signal Recovery with Nonparametric Noise Distributions – We present a framework for learning sparse representations for a signal that is more sensitive to noise than the ones it is trained on. We present a greedy algorithm to compute the Hessian of the training signal using a nonhomogeneous dictionary. The Hessian is a dictionary that can contain arbitrary noise. A learning task in this setting is to learn a dictionary that incorporates the noise of a noisy sample. We propose a novel approach by which the noise of the sample is partitioned into sparse and nonsmooth units. Our algorithm is guaranteed to find the Hessian when the noise in the samples is nonhomogeneous noise. Compared to a nonhomogeneous dictionary learning, our algorithm is more scalable and more robust to the noise than the sparse dictionary model, and it can learn the Hessian more efficiently if the input data is noisy. The experimental results demonstrate that both learning and learning are better than the sparse dictionary learning on both synthetic and real datasets.

Generative Adversarial Networks (GANs) have been widely used to generate image based models to learn images, which have been a focus of many researchers in recent years. However, many applications of GANs have not been examined and the literature onGANs in this area is still sparse and very limited. We give an overview of a recently developed deep neural network based onGAN and compare it against other proposed deep learning based onGAN models. We show that deep learning based onGAN models are more robust to variations in the input, as compared to other deep learning based onGAN models trained on the same input. Moreover, we compare various other deep learning based onGAN models which are not used in the literature. Finally, we examine how such a feature rich input representation plays a role in learning deep generative models for classification tasks.

Multitask Learning for Knowledge Base Linking via Neural-Synthesis

A Deep Convolutional Auto-Encoder for Semi-Supervised Learning with Missing and Largest Vectors

Matching-Gap Boosting for Sparse Signal Recovery with Nonparametric Noise Distributions

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  • Image quality assessment by non-parametric generalized linear modeling

    A Fast Approach to Classification Using Linear and Nonlinear Random FieldsGenerative Adversarial Networks (GANs) have been widely used to generate image based models to learn images, which have been a focus of many researchers in recent years. However, many applications of GANs have not been examined and the literature onGANs in this area is still sparse and very limited. We give an overview of a recently developed deep neural network based onGAN and compare it against other proposed deep learning based onGAN models. We show that deep learning based onGAN models are more robust to variations in the input, as compared to other deep learning based onGAN models trained on the same input. Moreover, we compare various other deep learning based onGAN models which are not used in the literature. Finally, we examine how such a feature rich input representation plays a role in learning deep generative models for classification tasks.


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