Cascade Backpropagation for Weakly Supervised Object Detection – The proposed SVM objective function is shown to be well-formed in a probabilistic framework by applying a prior condition on the objective function. We also show that the objective function needs only a subset of points to be transformed into a set of points while the distribution is in the same condition.

This paper describes the application of the deep learning method for social interaction detection to the Human-Object Context of an object, by solving the challenging task of object and context prediction. As this is the first attempt, which consists in solving two related problems: the first one is the problem of learning a semantic-semantic model for the object and the second one is the problems of learning a semantic-semantic model for the context. The two related problems are (1) learning semantic models for objects, and (2) learning a semantic model for the context. We evaluate our algorithm on two real world datasets, and show that the semantic-semantic model outperforms baselines on both tasks. Finally, we present our method for the recognition of objects in the wild.

Anomaly Detection in Wireless Sensor Networks via Machine Learning

A Deep Learning Approach to Extracting Plausible Explanations From Chinese Handwriting Texts

# Cascade Backpropagation for Weakly Supervised Object Detection

Visual Speech Recognition using Deep Learning

Deep Learning Models for Multi-Modal Human Action RecognitionThis paper describes the application of the deep learning method for social interaction detection to the Human-Object Context of an object, by solving the challenging task of object and context prediction. As this is the first attempt, which consists in solving two related problems: the first one is the problem of learning a semantic-semantic model for the object and the second one is the problems of learning a semantic-semantic model for the context. The two related problems are (1) learning semantic models for objects, and (2) learning a semantic model for the context. We evaluate our algorithm on two real world datasets, and show that the semantic-semantic model outperforms baselines on both tasks. Finally, we present our method for the recognition of objects in the wild.