A deep regressor based on self-tuning for acoustic signals with variable reliability – The problem of robust multi-class classification remains understudied. The multi-class classification problem is known to be non-trivial and has been tackled by the classification of non-differentiable classifiers. Among the best existing state-of-the-art algorithms are the standard linear classifier, which is very efficient, and the spectral classifier, which is based on spectral clustering. However, spectral clustering is not widely used as a discriminative technique, and most of the existing algorithms do not require spectral clustering. We propose a multi-class multi-class clustering algorithm, based on a new spectral clustering algorithm, and establish that a simple regularization bound is necessary to guarantee the optimal clustering. We show that the proposed algorithm achieves state-of-the-art performance on three benchmark datasets and demonstrate its effectiveness on one publicly available dataset.

Deep object detectors often provide a large amount of useful contextual information in order to improve classification accuracy. However, they are also prone to overfitting. Recent work on deep-dense recurrent neural network (CNN) has achieved a significant improvement in classification accuracy. However, CNNs can suffer from overfitting at a crucial level. In order to address this issue, we propose a novel framework to learn features from deep CNNs. Specifically, CNNs learn models for object categories, i.e. those with multiple classifiers. They are capable of incorporating contextual information and making predictions from the CNN’s features, so that the classification accuracy of the CNN is directly dependent on the semantic information provided by the object categories. In our work, we propose a simple and efficient learning procedure to learn object categories, in order to improve classification accuracy on CNNs trained on object categories. The proposed approach shows promising performance compared to other deep CNNs and a state-of-the-art CNN. We will evaluate this approach, and illustrate it in various applications.

A Review on Fine Tuning for Robust PCA

Towards Big Neural Networks: Analysis of Deep Learning Techniques on Diabetes Prediction

A deep regressor based on self-tuning for acoustic signals with variable reliability

Convergence Properties of Binary Convolutions

Clustering-Based Feature Representations for Person Re-IdentificationDeep object detectors often provide a large amount of useful contextual information in order to improve classification accuracy. However, they are also prone to overfitting. Recent work on deep-dense recurrent neural network (CNN) has achieved a significant improvement in classification accuracy. However, CNNs can suffer from overfitting at a crucial level. In order to address this issue, we propose a novel framework to learn features from deep CNNs. Specifically, CNNs learn models for object categories, i.e. those with multiple classifiers. They are capable of incorporating contextual information and making predictions from the CNN’s features, so that the classification accuracy of the CNN is directly dependent on the semantic information provided by the object categories. In our work, we propose a simple and efficient learning procedure to learn object categories, in order to improve classification accuracy on CNNs trained on object categories. The proposed approach shows promising performance compared to other deep CNNs and a state-of-the-art CNN. We will evaluate this approach, and illustrate it in various applications.