Top Research Papers on Deep Learning

This textbook gives a comprehensive understanding of the foundational ideas and key concepts of modern deep learning architectures and techniques.

This article provides a conceptual distinction between relevant terms and concepts, explains the process of automated analytical model building through machine learning and deep learning, and discusses the challenges that arise when implementing such intelligent systems in the field of electronic markets and networked business.

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.

A systematic overview of trends, challenges, and opportunities in applications of deep-learning methods in seismology is presented, covering approaches, limitations, and opportunity.

With a direct analysis of neural networks, this paper presents a mathematically tight generalization theory to partially address an open problem regarding the generalization of deep learning.Unlike previous bound-based theory, our main theory is quantitatively as tight as possible for every dataset individually, while producing qualitative insights competitively.Our results give insight into why and how deep learning can generalize well, despite its large capacity, complexity, possible algorithmic instability, nonrobustness, and sharp minima, answering to an open question in the literature.We ...

This is the first comprehensive and self-contained introduction to deep reinforcement learning, covering all aspects from fundamentals and research to applications. It includes examples and codes to help readers practice and implement the techniques.

How can neural networks learn the rich internal representations required for difficult tasks such as recognizing objects or understanding language?

This book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both as function universal approximators and information processors. The book bridges the gap between ideas and concepts of neural networks, which are used nowadays at an intuitive level, and the precise modern mathematical language, presenting the best practices of the former and enjoying the robustness and elegance of the latter. This book can be used in a graduate course in deep learning, with the first few parts being accessible to senior undergraduates. In a...

In the past few years, deep learning has been successfully applied to various omics data. However, the applications of deep learning in metabolomics are still relatively low compared to others omics. Currently, data pre-processing using convolutional neural network architecture appears to benefit the most from deep learning. Compound/structure identification and quantification using artificial neural network/deep learning performed relatively better than traditional machine learning techniques, whereas only marginally better results are observed in biological interpretations. Before deep learn...

Compared to traditional machine learning, deep learning can learn deeper abstract data representation and understand scattered data properties. It has gained considerable attention for its extraordinary performances. However, existing deep learning algorithms perform poorly on new tasks. Meta-learning, known as learning to learn, is one of the effective techniques to overcome this issue. Meta-learning’s generalization ability to unknown tasks is improved by employing prior knowledge to assist the learning of new tasks. There are mainly three types of meta-learning methods: metric-based, model-...

This paper is using deep learning to uncover higher-level representational features, to clearly explain transfer learning, to provide current solutions and evaluate applications in diverse areas of transfer learning as well as deep learning.

Reinforcement learning is a learning paradigm for solving sequential decision-making problems. Recent years have witnessed remarkable progress in reinforcement learning upon the fast development of deep neural networks. Along with the promising prospects of reinforcement learning in numerous domains such as robotics and game-playing, transfer learning has arisen to tackle various challenges faced by reinforcement learning, by transferring knowledge from external expertise to facilitate the efficiency and effectiveness of the learning process. In this survey, we systematically investigate the r...

An insight survey for machine learning along with deep learning applications in various domains is provided and some applications with new normal COVID-19 blues are exemplified.

This review of talks and discussions in the "Deep Learning and Reinforcement Learning" session of the symposium, International Symposium on Artificial Intelligence and Brain Science, discusses whether the authors can achieve comprehensive understanding of human intelligence based on the recent advances of deep learning and reinforcement learning algorithms.

This survey systematically investigates the recent progress of transfer learning approaches in the context of deep reinforcement learning, and provides a framework for categorizing the state-of-the-art transfer Learning approaches under which to analyze their goals, methodologies, compatible reinforcement learning backbones, and practical applications.

It is concluded that ML and DL are powerful new tools for predictive modelling and data analysis, comparable to other traditional statistical tools.

A state-of-the-art survey on an intelligent robot with the capability of autonomous deciding and learning reveals that the latest research in deep learning and reinforcement learning has paved the way for highly complex tasks to be performed by robots.

This is the first comprehensive survey in the efficient deep learning space that covers the landscape of model efficiency from modeling techniques to hardware support and the seminal work there.

This work explores the suitability of adopting AutoDL for Water Quality Assessment by drawing a comparison between AutoDL and a conventional models and analysis to foresee the quality of the water, an appropriate class based on Water Quality Index segregating water bodies into different classes.

This paper aims to introduce deep learning to the power quality community by reviewing the latest applications and discussing the open challenges of this technology. Publications covering deep learning to power quality are stratified in terms of application, type of data, and learning technique. This work shows that the majority of the deep learning applications to power quality are based on unrealistic synthetic data and supervised learning without proper labelling. Some applications with deep learning have already been solved by previous machine learning methods or expert systems. The main b...