Text Summarization as Tree Transduction by Top-Down TreeLSTM

Iterative Proportional Fitting The iterative proportional fitting procedure (IPFP, also known as biproportional fitting in statistics, RAS algorithm in economics and matrix ranking or matrix scaling in computer science) is an iterative algorithm for estimating cell values of a contingency table such that the marginal totals remain fixed and the estimated table decomposes into an outer product. …

“The end goal of pervasive analytics is simple and will change the way in which the world operates today. By feeding individuals the right information, at the right time, analytics become invisible and embedded into every application and workflow of every user. It is a vision, a goal, a strategy that every individual across every industry can rally around in order to drive the business metrics that matter through the use of data.” TJ Laher ( November 14, 2014 )

Longitudinal Regression Trees and Forests (splinetree)Builds regression trees and random forests for longitudinal or functional data using a spline projection method. Implements and extends the work of Yu …

People pointed me to various recent news articles on the retirement from the Cornell University business school of eating-behavior researcher and retraction king Brian Wansink.

In this blog post, we’ll show you how to deploy a TensorFlow object detection model to AWS DeepLens. This enables AWS DeepLens to perform real-time object detection using the built-in camera. Object detection is the technique for machines to correctly identify different objects in the image or video. Image recognition, specifically object detection is a very interesting topic in the AI deep learning world. For example, in autonomous driving, the camera on the vehicle first needs to be able to detect objects (such as people, cars, and signs) on the road before making any decisions to turn, slow down, or stop.

In this post we are going to walk through the details and intuition behind simple convolution operation as one one of the most fundamental concept in Computer Vision. Additionally we will build a Java Application GUI which uses different convolution filters(implemented purely in java) to transform images of your choice. Please find the free open source code at this github repository as part of Packt Java Machine Learning for Computer Vision Course.

Data science is the business of learning from data, which is traditionally the business of statistics. Data science, however, is often understood as a broader, task-driven and computationally-oriented version of statistics. Both the term data science and the broader idea it conveys have origins in statistics and are a reaction to a narrower view of data analysis. Expanding upon the views of a number of statisticians, this paper encourages a big-tent view of data analysis. We examine how evolving approaches to modern data analysis relate to the existing discipline of statistics (e.g. exploratory analysis, machine learning, reproducibility, computation, communication and the role of theory). Finally, we discuss what these trends mean for the future of statistics by highlighting promising directions for communication, education and research. Data Science vs. Statistics: Two Cultures

Annotation and segmentation of medical images is a laborious endeavor that can be automated in part via deep learning (DL) techniques. These approaches have achieved state-of-the-art results in generic segmentation tasks, the goal of which is to classify images at the pixel level.