TernGrad: Temporal Trees that scale to the error of Measurements – We show how to use the data to detect and classify various types of events that occur in medical applications. The data is collected on medical data from a patient during the course of their stay at the University of California at Irvine. The study focuses on the development of a deep learning based machine learning method that detects the signs of a patient’s symptoms in an MRI. The detection method uses several classes of event detection methods, and we show how to use them to perform the medical diagnosis and recognition by using an automatic system.
We present a technique to classify noisy object images at low-level semantic similarity, based on the notion of unsupervised feature-level classification. We analyze the two main features of images: their spatial resolution and their intrinsic similarity. Then, we design a neural network algorithm that can efficiently and accurately predict their semantic similarity according to their spatial resolution with strong training in terms of both their geometric and spatial similarity. Such a method effectively combines both spatial resolution and visual similarity in the training data. To show their effectiveness and their superiority, we compare their performance to a deep learning based semantic image retrieval method. We show that the proposed method significantly outperforms both techniques for both classification and categorization tasks. In addition to this, we further propose a deep convolutional neural network architecture to learn the semantic cues from images. The proposed framework is able to perform semantic classification and categorization tasks effectively for both semantic and spatial information in image datasets.
Learning the Topic Representations Axioms of Relational Datasets
TernGrad: Temporal Trees that scale to the error of Measurements
Distributed Online Learning: A Bayesian Approach
Deep Neural Networks for Stochastic Optimization via Robust EstimationWe present a technique to classify noisy object images at low-level semantic similarity, based on the notion of unsupervised feature-level classification. We analyze the two main features of images: their spatial resolution and their intrinsic similarity. Then, we design a neural network algorithm that can efficiently and accurately predict their semantic similarity according to their spatial resolution with strong training in terms of both their geometric and spatial similarity. Such a method effectively combines both spatial resolution and visual similarity in the training data. To show their effectiveness and their superiority, we compare their performance to a deep learning based semantic image retrieval method. We show that the proposed method significantly outperforms both techniques for both classification and categorization tasks. In addition to this, we further propose a deep convolutional neural network architecture to learn the semantic cues from images. The proposed framework is able to perform semantic classification and categorization tasks effectively for both semantic and spatial information in image datasets.