Deep Learning for Real-Time Financial Transaction Graphs with Confounding Effects of Connectomics – Deep learning has been shown to improve over classical neural modeling in a variety of challenging applications. However, deep learning is still very difficult to learn. In this paper, we report on Deep Neural Networks (DNNs), a new architecture for object detection and classification using Convolutional Neural Networks (CNNs) that is capable of handling massive amounts of data. The architecture consists of three basic classes. The first one uses Convolutional Neural Network (CNN) to learn features from large data. The second one uses recurrent neural network (RNN) to learn features. The second and third class are learned using sparse binary code and the data in the first class is used to learn features from the second class. The performance of all the algorithms is evaluated on the tasks of object and visual detection. The results show how deep learning with CNNs can improve performance in these tasks.
We present a new approach to deep learning that combines a learned representation of the problem with a supervised learning method. We propose a novel learning method that relies on supervised deep generative models to learn to represent a model in the domain space as a discrete vector space with a given size and model-class. Our approach leverages a deep learning architecture that uses an LSTM classifier to learn to represent a model in the domain space as a 2D vector space. Our system provides a supervised representation of the domain and a representation of its model. We show that our system can be used to perform well in a variety of applications, for example, semantic image segmentation, and video summarization.
Recurrent Neural Networks for Activity Recognition in Video Sequences
A Semi-automated Test and Evaluation System for Multi-Person Pose Estimation
Deep Learning for Real-Time Financial Transaction Graphs with Confounding Effects of Connectomics
A Generalized Baire Gradient Method for Gaussian Graphical Models
A Deep Generative Model for 3D Object Recognition with Densely Convolutional Neural NetworksWe present a new approach to deep learning that combines a learned representation of the problem with a supervised learning method. We propose a novel learning method that relies on supervised deep generative models to learn to represent a model in the domain space as a discrete vector space with a given size and model-class. Our approach leverages a deep learning architecture that uses an LSTM classifier to learn to represent a model in the domain space as a 2D vector space. Our system provides a supervised representation of the domain and a representation of its model. We show that our system can be used to perform well in a variety of applications, for example, semantic image segmentation, and video summarization.