Fast, Accurate Metric Learning – Many machine learning applications are designed to handle small samples, in order to reduce the variance in the prediction model in the context of a large training set. The goal is to estimate the model’s predictive ability by means of the prediction metric defined as a pair of features of the same data pair, and to estimate the metric by means of a linear combination of these two features. In this work, we provide a novel method for estimating the metric in a deep learning setting, which we call ResNet-1. ResNet-1 is trained as a deep neural network to predict a single-label classification task for one of a large training set. It is trained using a large vocabulary of labeled data samples collected from a machine-learning classifier, whose predictions are aggregated as inputs, and then trained to predict the label distributions corresponding to the labeled data samples. Experiments on MS-COCO, CIMBA, and the large-scale MNIST dataset show that ResNet-1 consistently outperforms the trained deep learning model for predicting label distributions.

This paper presents a new deep learning based method for automatically recognizing human activities from visual imagery. Our method is based on the notion of an attribute as determined by using a visual feature vector on a set of objects. Our method applies a discriminant analysis tool to extract and extract visual features and to extract features that are not directly related to human activities, and to remove the extraneous information. The model is used to extract human activities, their relationships to the visual features and to reconstruct them using a 3D object-oriented mapping algorithm. Experiments on real-world data suggest that the proposed methods have significant performance advantages over previous models in both recognition accuracy and retrieval time.

A Multiunit Approach to Optimization with Couples of Units

Deep Neural Networks and Multiscale Generalized Kernels: Generalization Cost Benefits

Fast, Accurate Metric Learning

Multi-Instance Dictionary Learning in the Matrix Space with Applications to Video Classification

Image Captioning using Multiple Co-ordinate PatternsThis paper presents a new deep learning based method for automatically recognizing human activities from visual imagery. Our method is based on the notion of an attribute as determined by using a visual feature vector on a set of objects. Our method applies a discriminant analysis tool to extract and extract visual features and to extract features that are not directly related to human activities, and to remove the extraneous information. The model is used to extract human activities, their relationships to the visual features and to reconstruct them using a 3D object-oriented mapping algorithm. Experiments on real-world data suggest that the proposed methods have significant performance advantages over previous models in both recognition accuracy and retrieval time.