Anomaly Detection in Multivariate Non-stationary Time Series for Automatic DBMS Diagnosis ABSTRACT— Anomaly detection in database management systems (DBMSs) is difficult because of increasing number of statistics (stat) and event metrics in big data system.
ABSTRACT In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting.
Abstract We develop an algorithm which exceeds the performance of board certified cardiologists in detecting a wide range of heart arrhythmias from electrocardiograms recorded with a single-lead wearable monitor.
Abstract Learning to Optimize (Li & Malik, 2016) is a recently proposed framework for learning optimization algorithms using reinforcement learning.
Abstract: Batch Normalization is a commonly used trick to improve the training of deep neural networks.
Abstract: Deep learning is the state-of-the-art in fields such as visual object recognition and speech recognition.
Motivation: Tumor classification using Imaging Mass Spectrometry (IMS) data has a high potential for future applications in pathology.
Please, show this post of Ben Lorica’s podcast: Adoption of machine learning and deep learning in large companies Everything in the enterprise space is ROI driven.
One of my biggest mistakes was to make my whole master’s degrees dissertation using private data (provided by my former employer) using closed tools (e.g.