Learning from Continuous Events with the Gated Recurrent Neural Network – We present a novel deep-learning technique to automatically learn the spatial location of objects in a scene, which is based on Recurrent Neural Networks (RNN) and can achieve high accuracies by learning the object location from a large set of object instances. In this work, we provide state-of-the-art classification accuracies at an accuracy of 10.81%. Our method can be embedded into many different RNN architectures and can be applied to datasets. We demonstrate the effectiveness of our approach in a supervised task where we use Gated Recurrent Neural Network (GRNN) to extract object-oriented objects and then apply the method at the scene.

Most computer vision systems consist of multiple layers of feature maps over a large variety of visual concepts. Therefore, different layers of feature maps may contain different object classes and different attributes, so the appearance and learning of a system can be complex. In this work, we propose an image feature model to encode object class information based on the appearance of each object. We propose an efficient embedding learning algorithm by embedding information into a simple yet effective feature matrix. We also show how to automatically classify the object attributes using the matrix. We illustrate the learning process through various experiments on a large dataset of images of birds. Experimental results on three datasets demonstrate the effectiveness of our method.

On a Generative Baseline for Modeling Clinical Trials

Feature Selection with Stochastic Gradient Descent in Nonconvex and Nonconjugate Linear Models

Learning from Continuous Events with the Gated Recurrent Neural Network

Boosting for the Development of Robotic Surgery

Learning A CNN Model for Object Recognition Using Human-derived AttributesMost computer vision systems consist of multiple layers of feature maps over a large variety of visual concepts. Therefore, different layers of feature maps may contain different object classes and different attributes, so the appearance and learning of a system can be complex. In this work, we propose an image feature model to encode object class information based on the appearance of each object. We propose an efficient embedding learning algorithm by embedding information into a simple yet effective feature matrix. We also show how to automatically classify the object attributes using the matrix. We illustrate the learning process through various experiments on a large dataset of images of birds. Experimental results on three datasets demonstrate the effectiveness of our method.