Chapter 16. Abductive Learning

2021 ◽  
Author(s):  
Zhi-Hua Zhou ◽  
Yu-Xuan Huang
Keyword(s):  
Machine Learning ◽  
Logical Reasoning ◽  
New Paradigm

This chapter will introduce ABL (ABductive Learning), a new paradigm which integrates machine learning and logical reasoning in a balanced loop enabling them to work together in a mutually beneficial way.

scholarly journals In the Pursuit of Privacy: The Promises and Predicaments of Federated Learning in Healthcare

2021 ◽  
Vol 4 ◽  
Author(s):  
Mustafa Y. Topaloglu ◽  
Elisabeth M. Morrell ◽  
Suraj Rajendran ◽  
Umit Topaloglu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Dice Coefficient ◽  
New Paradigm ◽  
Underrepresented Groups ◽  
Patient Protection ◽  
Shapley Values ◽  
Geographically Distributed ◽  
Computationally Expensive ◽  
Improved Performance

Artificial Intelligence and its subdomain, Machine Learning (ML), have shown the potential to make an unprecedented impact in healthcare. Federated Learning (FL) has been introduced to alleviate some of the limitations of ML, particularly the capability to train on larger datasets for improved performance, which is usually cumbersome for an inter-institutional collaboration due to existing patient protection laws and regulations. Moreover, FL may also play a crucial role in circumventing ML’s exigent bias problem by accessing underrepresented groups’ data spanning geographically distributed locations. In this paper, we have discussed three FL challenges, namely: privacy of the model exchange, ethical perspectives, and legal considerations. Lastly, we have proposed a model that could aide in assessing data contributions of a FL implementation. In light of the expediency and adaptability of using the Sørensen–Dice Coefficient over the more limited (e.g., horizontal FL) and computationally expensive Shapley Values, we sought to demonstrate a new paradigm that we hope, will become invaluable for sharing any profit and responsibilities that may accompany a FL endeavor.

scholarly journals Clustering and enhanced classification using a hybrid quantum autoencoder

2021 ◽  
Author(s):  
Maiyuren Srikumar ◽  
Charles Daniel Hill ◽  
Lloyd Hollenberg
Keyword(s):  
Machine Learning ◽  
Supervised Classification ◽  
Quantum Information Theory ◽  
Quantum States ◽  
Data Sets ◽  
New Paradigm ◽  
Novel Approach ◽  
Representational Space ◽  
Quantum Machine Learning ◽  
Pure States

Abstract Quantum machine learning (QML) is a rapidly growing area of research at the intersection of classical machine learning and quantum information theory. One area of considerable interest is the use of QML to learn information contained within quantum states themselves. In this work, we propose a novel approach in which the extraction of information from quantum states is undertaken in a classical representational-space, obtained through the training of a hybrid quantum autoencoder (HQA). Hence, given a set of pure states, this variational QML algorithm learns to identify – and classically represent – their essential distinguishing characteristics, subsequently giving rise to a new paradigm for clustering and semi-supervised classification. The analysis and employment of the HQA model are presented in the context of amplitude encoded states – which in principle can be extended to arbitrary states for the analysis of structure in non-trivial quantum data sets.

Emerging Technologies and Universities

2021 ◽  
pp. 97-114
Author(s):  
Fausto E. Jacome
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
New Technologies ◽  
Emerging Technologies ◽  
The Internet ◽  
New Paradigm ◽  
New Era ◽  
Technological Revolution ◽  
Goods And Services ◽  
The Internet Of Things

Emerging technologies such as machine learning, the cloud, the internet of things (IoT), social web, mobility, robotics, and blockchain, among others, are powering a technological revolution in such a way that are transforming all human activities. These new technologies have generated creative ways of offering goods and services. Today's consumers demand in addition to quality, innovation, a real-time and ubiquitous service. In this context, what is the challenge that academy faces? What is the effect of these new technologies on the universities mission? What are people's expectations about academy in this new era? This chapter tries to get answers to these questions and explain how these emerging technologies are converting universities to lead society transformation to the digital age. Under this new paradigm, there are only two roads: innovate or perish. As might be expected universities are embracing these technologies for innovating themselves.

Reconstruction of GRACE Total Water Storage Through Automated Machine Learning

2020 ◽  
Author(s):  
Alex Sun ◽  
Bridget Scanlon ◽  
Himanshu Save ◽  
Ashraf Rateb
Keyword(s):  
Machine Learning ◽  
Spatial Variability ◽  
Large Scale ◽  
Water Storage ◽  
Total Water ◽  
New Paradigm ◽  
Satellite Mission ◽  
Automated Machine Learning ◽  
The Mean ◽  
The Impact

<p>The GRACE satellite mission and its follow-on, GRACE-FO, have provided unprecedented opportunities to quantify the impact of climate extremes and human activities on total water storage at large scales. The approximately one-year data gap between the two GRACE missions needs to be filled to maintain data continuity and maximize mission benefits. There is strong interest in using machine learning (ML) algorithms to reconstruct GRACE-like data to fill this gap. So far, most studies attempted to train and select a single ML algorithm to work for global basins. However, hydrometeorological predictors may exhibit strong spatial variability which, in turn, may affect the performance of ML models. Existing studies have already shown that no single algorithm consistently outperformed others over all global basins. In this study, we applied an automated machine learning (AutoML) workflow to perform GRACE data reconstruction. AutoML represents a new paradigm for optimal model structure selection, hyperparameter tuning, and model ensemble stacking, addressing some of the most challenging issues related to ML applications. We demonstrated the AutoML workflow over the conterminous U.S. (CONUS) using six types of ML algorithms and multiple groups of meteorological and climatic variables as predictors. Results indicate that the AutoML-assisted gap filling achieved satisfactory performance over the CONUS. For the testing period (2014/06–2017/06), the mean gridwise Nash-Sutcliffe efficiency is around 0.85, the mean correlation coefficient is around 0.95, and the mean normalized root-mean square error is about 0.09. Trained models maintain good performance when extrapolating to the mission gap and to GRACE-FO periods (after 2017/06). Results further suggest that no single algorithm provides the best predictive performance over the entire CONUS, stressing the importance of using an end-to-end workflow to train, optimize, and combine multiple machine learning models to deliver robust performance, especially when building large-scale hydrological prediction systems and when predictor importance exhibits strong spatial variability.</p>

Machine learning as a new paradigm for characterizing localization and lateralization of neuropsychological test data in temporal lobe epilepsy

2018 ◽  
Vol 86 ◽  
pp. 58-65 ◽  
Author(s):  
Brandon Frank ◽  
Landon Hurley ◽  
Travis M. Scott ◽  
Pat Olsen ◽  
Patricia Dugan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Temporal Lobe Epilepsy ◽  
Temporal Lobe ◽  
Test Data ◽  
Neuropsychological Test ◽  
New Paradigm

scholarly journals Automated Reasoning for Explainable Artificial Intelligence

10.29007/4b7h ◽  
2018 ◽  
Author(s):  
Maria Paola Bonacina
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Automated Reasoning ◽  
Position Statement ◽  
Short Paper ◽  
Future Research ◽  
Grand Challenge ◽  
New Paradigm ◽  
Research Areas ◽  
Explainable Artificial Intelligence

Reasoning and learning have been considered fundamental features of intelligence ever since the dawn of the field of artificial intelligence, leading to the development of the research areas of automated reasoning and machine learning. This short paper is a non-technical position statement that aims at prompting a discussion of the relationship between automated reasoning and machine learning, and more generally between automated reasoning and artificial intelligence. We suggest that the emergence of the new paradigm of XAI, that stands for eXplainable Artificial Intelligence, is an opportunity for rethinking these relationships, and that XAI may offer a grand challenge for future research on automated reasoning.

scholarly journals Machine Learning and Urban Drainage Systems: State-of-the-Art Review

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3545
Author(s):  
Soon-Ho Kwon ◽  
Joong-Hoon Kim
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Urban Drainage ◽  
Complex Data ◽  
New Paradigm ◽  
Neural Network Models ◽  
Drainage Systems ◽  
Time Operation ◽  
Urban Drainage Systems ◽  
Scientific Engineering

In the last decade, machine learning (ML) technology has been transforming daily lives, industries, and various scientific/engineering disciplines. In particular, ML technology has resulted in significant progress in neural network models; these enable the automatic computation of problem-relevant features and rapid capture of highly complex data distributions. We believe that ML approaches can address several significant new and/or old challenges in urban drainage systems (UDSs). This review paper provides a state-of-the-art review of ML-based UDS modeling/application based on three categories: (1) operation (real-time operation control), (2) management (flood-inundation prediction) and (3) maintenance (pipe defect detection). The review reveals that ML is utilized extensively in UDSs to advance model performance and efficiency, extract complex data distribution patterns, and obtain scientific/engineering insights. Additionally, some potential issues and future directions are recommended for three research topics defined in this study to extend UDS modeling/applications based on ML technology. Furthermore, it is suggested that ML technology can promote developments in UDSs. The new paradigm of ML-based UDS modeling/applications summarized here is in its early stages and should be considered in future studies.

A Survey of Deep Learning and Its Applications: A New Paradigm to Machine Learning

2019 ◽  
Vol 27 (4) ◽  
pp. 1071-1092 ◽  
Author(s):  
Shaveta Dargan ◽  
Munish Kumar ◽  
Maruthi Rohit Ayyagari ◽  
Gulshan Kumar
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
New Paradigm
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