The success of machine learning algorithms generally depends on data representation, and
we hypothesize that this is because different representations can entangle and hide more or …

The success of machine learning algorithms generally depends on data representation, and
we hypothesize that this is because different representations can entangle and hide more or …

An introduction to a broad range of topics in deep learning, covering mathematical and
conceptual background, deep learning techniques used in industry, and research …

Inventors have long dreamed of creating machines that think. Ancient Greek myths tell of
intelligent objects, such as animated statues of human beings and tables that arrive full of …

The KNN algorithm is one of the most popular data mining algorithms. It has been widely
and successfully applied to data analysis applications across a variety of research topics in …

This paper presents a hybrid model of integrating the synergy of two superior classifiers:
Convolutional Neural Network (CNN) and Support Vector Machine (SVM), which have …

Theoretical results suggest that in order to learn the kind of complicated functions that can
represent high-level abstractions (eg, in vision, language, and other AI-level tasks), one may …

The moments (aka, mean and standard deviation) of latent features are often removed as
noise when training image recognition models, to increase stability and reduce training time …

The aim of this book is to discuss the fundamental ideas which lie behind the statistical
theory of learning and generalization. It considers learning as a general problem of function …

Standard approaches to computing interpolated (in-between) frames in a video sequence
require accurate pixel correspondences between images eg using optical flow. We present …