Data Research Papers

Regularization for Deep Learning: A Taxonomy

Regularization is one of the crucial ingredients of deep learning, yet the term regularization has various definitions, and regularization methods are often studied separately from each other. In our work we present a systematic, unifying t...

An introduction to deep learning

Deep learning allows automatically learning multiple levels of representations of the underlying distribution of the data to be modeled. In this work, a specific implementation called stacked denoising autoencoders is explored. We contribut...

Research on a Decision Prediction Method Based on Causal Inference and a Multi-Expert FTOPJUDGE Mechanism

Legal judgement prediction (LJP) is a crucial part of legal AI, and its goal is to predict the outcome of a case based on the information in the description of criminal facts. This paper proposes a decision prediction method based on causal...

A Deep Ensemble Learning Method for Effort-Aware Just-In-Time Defect Prediction

Since the introduction of just-in-time effort aware defect prediction, many researchers are focusing on evaluating the different learning methods, which can predict the defect inducing changes in a software product. In order to predict thes...

Deep learning for smart manufacturing: Methods and applications

Deep Learning for Entity Matching

Entity matching (EM) finds data instances that refer to the same real-world entity. In this paper we examine applying deep learning (DL) to EM, to understand DL's benefits and limitations. We review many DL solutions that have been develope...

Deep Learning in Mobile and Wireless Networking: A Survey

The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traff...

Synthetic Data for Deep Learning

A survey on deep learning for big data

Research Progress on Distributed Fiber Optic Event Recognition Methods Under Data Constraints

Due to the dynamic monitoring capability, Distributed Acoustic Sensing (DAS) has been widely deployed in various industrial scenarios. Deep learning greatly boosts the event detection accuracy of fiber sensing systems. Nevertheless, practic...

Survey on deep learning with class imbalance

The purpose of this study is to examine existing deep learning techniques for addressing class imbalanced data. Effective classification with imbalanced data is an important area of research, as high class imbalance is naturally inherent in...

Synthetic Data Pretraining for Hyperspectral Image Super-Resolution

Large-scale self-supervised pretraining of deep learning models is known to be critical in several fields, such as language processing, where its has led to significant breakthroughs. Indeed, it is often more impactful than architectural de...

Fairness in Machine Learning

Data Mining and Machine Learning

The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business an...

Foundations of Machine Learning

This chapter focuses on providing a complete grasp of the foundations of machine learning (ML). Machine learning is a rapidly evolving domain with wide-ranging applications, from natural language processing to autonomous vehicles. This chap...

Machine Learning

Fairness in Machine Learning: A Survey

When Machine Learning technologies are used in contexts that affect citizens, companies as well as researchers need to be confident that there will not be any unexpected social implications, such as bias towards gender, ethnicity, and/or pe...

Introduction to Machine Learning with Python: A Guide for Data Scientists

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teac...

Machine Learning and AI for Risk Management

We explore how machine learning and artificial intelligence (AI) solutions are transforming risk management. A non-technical overview is first given of the main machine learning and AI techniques of benefit to risk managementRisk management...

Predicting Stock Price Movements with Combined Deep Learning Models and Two-Tier Metaheuristic Optimization Algorithm

Predictions on stock market prices are a noble task owing to huge complex, dynamic, and chaotic surroundings. Fast ups and downs arise in the stock market due to influences from foreign merchandise, such as sensitive political, stockholder,...