Top Research Papers on Deep Learning

The results show that there is a certain degree of agreement concerning some aspects of what deep learning is, but there is also a strong need for clarification of the content and meaning of the construct of deep learning.

Computer vision and machine learning methods that utilize Convolutional Neural Networks and Long Short-Term Memory Networks for fish species identification, fish population density, and biomass estimation from underwater video sequences are described.

In teaching generally over the past twenty years, there has been a move towards teaching methods that encourage deep, rather than surface approaches to learning. The reason for this being that students, who adopt a deep approach to learning are considered to have learning outcomes of a better quality and desirability than those who adopt a surface approach to learning. However, how students approach their learning is still undervalued in Higher Education, as it is assumed that by the time a student enters higher education, he or she has already learned how to study. The purpose of this researc...

This paper primarily focuses on the precedents of the models above, examining how the initial ideas are assembled to construct the early models and how these preliminary models are developed into their current forms.

By obtaining the soft documents of this bayesian deep learning uncertainty in deep learning by online, you might not require more times to spend to go to the book start as competently as search for them.

In DEEP2, temporally focused structured light excites deep tissue in wide-field, and deep learning reconstructs clean images from scattered measurements in a computational multiphoton microscope to image through scattering tissue.

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

A deep learning framework for solving the problem of position and orientation estimation (DeePOE) of a radio frequency (RF) transmitter using the in-phase and quadrature-phase components of the RF signal data and a convolutional neural network which is designed to exploit latent features present within the received raw I/Q signal data.

This work focuses on deep knowledge in the form of deeply structured knowledge graphs, and introduces basic and advanced tensor models, which can be related to probabilistic graphical models, sum-product networks, and basis function models.

To develop a deep‐learning‐based image reconstruction framework for reproducible research in MRI, a network of supervised experiments and real-time measurements were used to demonstrate the power of deep learning in image reconstruction.

This tutorial discusses artificial neural networks, which are the basic building blocks of deep learning, and points out some of the many connections between deep learning and other not-so-deep techniques.

This new volume addresses a number of important issues related to reading and writing that were not attended to in the previous volume, Deep Reading: Teaching Reading in the Writing Classroom (NCTE 2017)—especially those related to identity, culture, and positionality. In this volume the authors address the broad question of equity and social justice in the acquisition and practice of literacy, and the multifaceted lived reality of positionality related especially to race, class, language, and gender as experienced by students in the classroom.

The DEEP-Hybrid-DataCloud project developed a distributed architecture to leverage intensive computing techniques such as needed for deep learning and provides distributed training facility for machine learning, artificial intelligence and deep learning via EOSC portal.

A deep-learning–based approach is presented for classifying some of the fossil record’s most widely documented yet vexing historical material—fossil pollen, paired with an ecological and climatic understanding of the distributions of plant groups today, which provides an important lens for paleobotanical diversity and data for paleoclimatic inference.

This paper draws a geometric picture of the deep learning system by finding its analogies with two existing geometric structures, the geometry of quantum computations and the diffeomorphic template matching to guide the design of the structures and algorithms of deep learning systems.

This paper introduces a deep learning aided method to incentivize credible sample contributions from selfish and rational agents and shows a connection between this sample elicitation problem and $f$-GAN, and how this connection can help reconstruct an estimator of the distribution based on collected samples.

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.

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.

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.

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 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 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.

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).

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...

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.

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.

音声や画像などを入力とする認 識問題(例えば発話内容), の状況について解説する(図 1).

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 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.

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

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.

The curse of dimensionality is a common problem for numerous machine learning methods when they are confronted with high-dimensional data. In order to deal with this problem a popular approach is the introduction of features vectors as a high-level abstraction of the input data. Designing task-specific features by hand is often very challenging and not very cost-efficient since a set of features that provides a good representation of the data for a task is usually worthless for all other tasks. Therefore, automatic feature learning is desirable. An interesting and successful approach for proce...

A comparative study of machine learning and deep learning is given in the paper and allows researcher to have a broad view on these techniques so that they can understand which one will be preferable solution for a particular problem.

Learning strategies are defined as behaviors and thoughts that a learner engages in during learning and that are intended to influence the learner’s encoding process. Today, demands for teaching how to learn increase, because there is a lot of complex material which is delivered to students. But learning strategies shouldn be identified as tricks of students for achieving high scores in exams. Cognitive researchers and theorists assume that learning strategies are related to two types of learning processing, which are described as ‘surface learning’ and ‘deep learning’. In addition learning st...

This paper provided a comprehensive analysis of current trends in software frameworks, Data Movement optimization strategies, sparsity, quantization and compression methods, using ML for architecture exploration, and DVFS, which provides strategies for maximizing hardware utilization and power consumption during training, machine learning, dynamic voltage, and frequency scaling, runtime systems.

Early years education at Scotch Oakburn College in Launceston, Tasmania, is inspired by the Reggio Emilia Education Project and strongly fosters inquiry learning in its educational approach.

The DeeDP system, which allows to detect vulnerabilities in C/C++ source code and generate patch for fixing detected issue, uses deep learning methods to organize rules for deciding whether a code fragment is vulnerable.

This work describes model-based optimization and data-centric deep learning as edges of a continuous spectrum varying in specificity and parameterization, and provides a tutorial-style presentation to the methodologies lying in the middle ground of this spectrum, referred to as model- based deep learning.

This article proposes and implements a DL-driven scheduler for DL clusters, targeting global training job expedition by dynamically resizing resources allocated to jobs, and implements DL techniques to enable dynamic resource scaling in DL jobs on MXNet.

An overview of the applications and advancements of DL and DRL in 6G networks is provided and the latest research is discussed to identify areas for further exploration in this field.

A new ODL framework is presented that attempts to tackle the challenges by learning DNN models which dynamically adapt depth from a sequence of training data in an online learning setting by proposing a novel Hedge Backpropagation method for online updating the parameters of DNN effectively.

This expository paper gives a brief introduction to the inner functioning of the new and successfull algorithms of Deep Learning and Geometric Deep Learning with a focus on Graph Neural Networks.

A new model-free RL algorithm based on a Bayesian deep model with deep kernel learning is adopted to learn the hidden complex action-value function instead of classical deep learning models, which could encode more uncertainty and fully take advantage of the replay memory.

This work designed and implemented a deep learning-based language learning Android app that provides both speech recognition and handwriting recognition and created Convolutional Neural Networks models that support dictation/handwriting training in Chinese and Japanese respectively.

An overall goal in this paper is a comprehensive explanation of the various Deep Reinforcement Learning (DRL) algorithms, and its combination with Multi-Agent methods.

The results show that the deep Chinese teaching and learning model is conducive to improving students’ discourse presentation ability and comprehensive skills, cultivating the learners’ autonomous learning ability and intercultural communication competence, and strengthening the integration of language teaching and cultural teaching.

This chapter describes the motivations for deep architecture, problem with large networks, the need fordeep architecture and new implementation techniques for deep learning, and an algorithm to implement the deep architecture using the recursive nature of functions and transforming them to get the desired output.