Deep Multitask Learning for Modeling Clinical Notes – The paper presents a method to train large-scale convolutional neural network (CNN) classifiers. The paper shows that it is possible to extract the relevant features, a critical step for classifying handwritten words. The approach is based on a modified version of the deep learning technique Deep-Sparse Networks. A large number of samples are collected every time, a method based on CNNs is proposed. The experiments show that the proposed method can improve the classification accuracy on an average of 78.9% of the samples that are collected by CNN classifier.

While existing state-of-the-art end-to-end visual object tracking algorithms often require expensive and memory-consuming re-entrant networks for training and decoding, the deep, end-to-end video matching protocol is an ideal framework to provide real-time performance improvement for end-to-end object tracking problems. In this work, we propose a simple yet effective approach to learn a deep end-to-end end object tracking network directly in a video by leveraging the temporal structure of the visual world. We first show that this approach can successfully learn end-to-end object tracking networks with good temporal structure, which is crucial for many end-to-end object tracking challenges. Next, we show that this end-to-end end-to-end visual object tracking network can achieve state-of-the-art end-to-end end-to-end performance on the ImageNet benchmark in real-time scenarios.

A Novel Approach to Facial Search and Generalization for Improving Appearance of Human Faces

Online Variational Gaussian Process Learning

Deep Multitask Learning for Modeling Clinical Notes

Bayesian Optimization for Learning Bayesian Optimization

Attention based Recurrent Neural Network for Video PredictionWhile existing state-of-the-art end-to-end visual object tracking algorithms often require expensive and memory-consuming re-entrant networks for training and decoding, the deep, end-to-end video matching protocol is an ideal framework to provide real-time performance improvement for end-to-end object tracking problems. In this work, we propose a simple yet effective approach to learn a deep end-to-end end object tracking network directly in a video by leveraging the temporal structure of the visual world. We first show that this approach can successfully learn end-to-end object tracking networks with good temporal structure, which is crucial for many end-to-end object tracking challenges. Next, we show that this end-to-end end-to-end visual object tracking network can achieve state-of-the-art end-to-end end-to-end performance on the ImageNet benchmark in real-time scenarios.