Top Research Papers on Deep Learning

Sebastian Flennerhag discusses his work on 'deep learning' in artificial intelligence, and its potential application in social science.

The goal of the tutorial is to introduce the recent developments of various deep learning methods to the KDD community, with a core focus on algorithms that can learn multi-layer hierarchies of representations, emphasizing their applications in information retrieval, object recognition, and speech perception.

This chapter discusses some widely used deep learning architecture and their practical applications in the agricultural field and their benefits and drawbacks.

This chapter analyzes the crisis of retrieval after providing the successful discussion of multimedia information retrieval that is the ability of retrieving an object of every multimedia.

The course starts from the basic concepts to understand, train and test neural networks for classification and regression and evolves to (Fully) Convolutional Neural Networks for image classification, object detection, and (semantic/instance) segmentation.

The state-of-the-art of deep learning is reviewed from a modeling and algorithmic perspective and a list of successful areas of applications in Artificial Intelligence (AI), Image Processing, Robotics and Automation is provided.

In this chapter, brief information about DL theory is given, advantages and disadvantages of deep learning are discussed, most used types of DNN are mentioned, popular DL architectures and frameworks are glanced and aimed to build smart systems for the finance and real estate domains.

Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.

Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks that have various differences from the structural and functional properties of biological brains.

Deep learning is a topic in the field of artificial intelligence (AI) and is a relatively new research area although based on the popular artificial neural networks (supposedly mirroring brain function).

This chapter empowers an entire assortment of independent frameworks where sensors, actuators, and registering hubs can cooperate and demonstrates that the falling design takes into account a free change in assessment speed on obliged gadgets while the misfortune in precision is kept to a base.

This chapter covers the basics ofDeep learning, different architectures of deep learning like artificial neural network, feed forward neural network), CNN, CNN, recurrent neuralnetwork, deep Boltzmann machine, and their comparison and summarizes the applications ofdeep learning in different areas.

A novel general-purpose crowd layer is proposed, which allows us to train deep neural networks end-to-end, directly from the noisy labels of multiple annotators, using only backpropagation.

This chapter provides a comprehensive explanation of deep learning including an introduction to ANNs, improving the deep NNs, CNNs, classic networks, and some technical tricks for image classification using deep learning.

This study shows that a deep learning - assisted diagnostic system with an artificial intelligence for grading diabetic retinopathy is a reliable alternative to diabetic retinopathy assessment, thus the use of this system may be a valuable tool in evaluating the DR.

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The compositional structure illustrated in the VGG network, and used in all of deep learning (this is where the “deep” comes from), has been incredibly successful in machine learning and artificial intelligence tasks over the last decade.

The compositional structure illustrated in the VGG network, and used in all of deep learning (this is where the “deep” comes from), has been incredibly successful in machine learning and artificial intelligence tasks over the last decade.

The advantages of deep learning over surface learning are well known. This article suggests that students can achieve deep learning effectively by using a dialectical approach. The Web provides a means of developing resources that will enable this goal to be achieved in a variety of ways. For part-time students lack of access and lack of tutor involvement in their work are common problems. The discussion in this article illustrates how a group of part-time postgraduate students achieved such goals in a social theory course. The article shows how dialectical thinking can operate and suggests wa...

Presented on August 20, 2018 from 12:15 p.m.-1:15 p.m. in the Georgia Tech Manufacturing Institute (GTMI), Auditorium.