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.

The recent popularity of online learning methods makes it particularly challenging for practitioners to learn online features. In this work, we propose a new algorithm, Deep Learning-RNN, for the task of modeling user opinion over textual content in both text and pictures. For this task, we trained Deep Learning-RNN to predict the first few sentences of a user’s text using a novel set of latent variables. This is done iteratively on a novel set of latent variables, the UserSentientTextset, which is a corpus of user comments on a text. We performed experiments on three popular datasets, MNIST, CIFAR-10, and CIFAR-100, with different experiments in terms of both the mean and variance of user comments predicting the first few sentences. We also performed experiments on a set of MNIST sentences where the accuracy was much better than that of users predicting the rest of the text and only marginally better than that of users predicting the entire set.

Tensor-based transfer learning for image recognition

Predicting Daily Activity with a Deep Neural Network

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

The Role of Intensive Regression in Learning to Play StarCraft

Supervised Feature Selection Using Graph Convolutional Neural NetworksThe recent popularity of online learning methods makes it particularly challenging for practitioners to learn online features. In this work, we propose a new algorithm, Deep Learning-RNN, for the task of modeling user opinion over textual content in both text and pictures. For this task, we trained Deep Learning-RNN to predict the first few sentences of a user’s text using a novel set of latent variables. This is done iteratively on a novel set of latent variables, the UserSentientTextset, which is a corpus of user comments on a text. We performed experiments on three popular datasets, MNIST, CIFAR-10, and CIFAR-100, with different experiments in terms of both the mean and variance of user comments predicting the first few sentences. We also performed experiments on a set of MNIST sentences where the accuracy was much better than that of users predicting the rest of the text and only marginally better than that of users predicting the entire set.