scholarly journals Applied Self-Supervised Learning: Review of the State-of-the-Art and Implementations in Medicine

2021 ◽  
Author(s):  
Alexander Chowdhury ◽  
Jacob Rosenthal ◽  
Jonathan Waring ◽  
Renato Umeton
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Large Scale ◽  
State Of The Art ◽  
The State ◽  
Unlabeled Data ◽  
Data Repositories ◽  
Public And Private ◽  
Complex Models ◽  
Definition Of

Machine learning has become an increasingly ubiquitous technology, as big data continues to inform and influence everyday life and decision-making. Currently in healthcare, as well as in most other industries, the two most prevalent machine learning paradigms are supervised learning and transfer learning. Both practices rely on large-scale, manually annotated datasets to train increasingly complex models. However, the requirement of data to be manually labeled leaves an excess of unused, unlabeled data available in both public and private data repositories. Self-supervised learning (SSL) is a growing area of machine learning that has the ability to take advantage of unlabeled data. Contrary to other machine learning paradigms, SSL algorithms create artificial supervisory signals from unlabeled data and pretrain algorithms on these signals. The aim of this review is two-fold: firstly, we provide a formal definition of SSL, divide SSL algorithms into their four unique subsets, and review the state-of-the-art published in each of those subsets between the years of 2014-2020. Second, this work surveys recent SSL algorithms published in healthcare, in order to provide medical experts with a clearer picture of how they can integrate SSL into their research, with the objective of leveraging unlabeled data.

scholarly journals Applying Self-Supervised Learning to Medicine: Review of the State of the Art and Medical Implementations

Informatics ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 59
Author(s):  
Alexander Chowdhury ◽  
Jacob Rosenthal ◽  
Jonathan Waring ◽  
Renato Umeton
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Large Scale ◽  
State Of The Art ◽  
The State ◽  
Unlabeled Data ◽  
Data Repositories ◽  
Public And Private ◽  
Complex Models ◽  
Definition Of

Machine learning has become an increasingly ubiquitous technology, as big data continues to inform and influence everyday life and decision-making. Currently, in medicine and healthcare, as well as in most other industries, the two most prevalent machine learning paradigms are supervised learning and transfer learning. Both practices rely on large-scale, manually annotated datasets to train increasingly complex models. However, the requirement of data to be manually labeled leaves an excess of unused, unlabeled data available in both public and private data repositories. Self-supervised learning (SSL) is a growing area of machine learning that can take advantage of unlabeled data. Contrary to other machine learning paradigms, SSL algorithms create artificial supervisory signals from unlabeled data and pretrain algorithms on these signals. The aim of this review is two-fold: firstly, we provide a formal definition of SSL, divide SSL algorithms into their four unique subsets, and review the state of the art published in each of those subsets between the years of 2014 and 2020. Second, this work surveys recent SSL algorithms published in healthcare, in order to provide medical experts with a clearer picture of how they can integrate SSL into their research, with the objective of leveraging unlabeled data.

scholarly journals LFO2: An Enhanced Version of Learning-From-OPT Caching Algorithm

2021 ◽  
Author(s):  
Yipkei Kwok ◽  
David L. Sullivan
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
State Of The Art ◽  
Window Size ◽  
Learning Model ◽  
The State ◽  
Small Window ◽  
Machine Learning Model ◽  
New Machine

Recent machine learning-based caching algorithm have shown promise. Among them, Learning-FromOPT (LFO) is the state-of-the-art supervised learning caching algorithm. LFO has a parameter named Window Size, which defines how often the algorithm generates a new machine-learning model. While using a small window size allows the algorithm to be more adaptive to changes in request behaviors, experimenting with LFO revealed that the performance of LFO suffers dramatically with small window sizes. This paper proposes LFO2, an improved LFO algorithm, which achieves high object hit ratios (OHR) with small window sizes. This results show a 9% OHR increase with LFO2. As the next step, the machine-learning parameters will be investigated for tuning opportunities to further enhance performance.

scholarly journals Artificial intelligence and machine learning in design of mechanical materials

2021 ◽  
Author(s):  
Kai Guo ◽  
Zhenze Yang ◽  
Chi-Hua Yu ◽  
Markus J. Buehler
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
State Of The Art ◽  
The State

This review revisits the state of the art of research efforts on the design of mechanical materials using machine learning.

scholarly journals Large-scale Semantic Parsing without Question-Answer Pairs

2014 ◽  
Vol 2 ◽  
pp. 377-392 ◽  
Author(s):  
Siva Reddy ◽  
Mirella Lapata ◽  
Mark Steedman
Keyword(s):  
Natural Language ◽  
Large Scale ◽  
Graph Matching ◽  
State Of The Art ◽  
The State ◽  
Semantic Parsing ◽  
Matching Problem ◽  
Weak Supervision ◽  
Benchmark Datasets

In this paper we introduce a novel semantic parsing approach to query Freebase in natural language without requiring manual annotations or question-answer pairs. Our key insight is to represent natural language via semantic graphs whose topology shares many commonalities with Freebase. Given this representation, we conceptualize semantic parsing as a graph matching problem. Our model converts sentences to semantic graphs using CCG and subsequently grounds them to Freebase guided by denotations as a form of weak supervision. Evaluation experiments on a subset of the Free917 and WebQuestions benchmark datasets show our semantic parser improves over the state of the art.

scholarly journals Social Potential of Non-Profi t Organizations

2021 ◽  
pp. 80-100
Author(s):  
V. M. NOVIKOV
Keyword(s):  
Large Scale ◽  
The State ◽  
Charitable Organizations ◽  
Specific Content ◽  
Non Profit ◽  
Regional Administration ◽  
Wide Range ◽  
Non Profit Organizations ◽  
Definition Of ◽  
Charitable Activities

There is a steady trend in the wide range of literature on the study of institutions: the definition of theoretical judgments often does not coincide and is not combined with the definition of general connections and patterns, which leads to ignoring the principle of systematic analysis of socio-economic processes. Indirectly, this means the priority of the random (individual) over the whole and general. Meanwhile, the concept of an institution correlates with the specific content of a phenomenon or process and is supplemented by a generalized and systematic approach. The study of such an urgent problem of the market economy as institutional choice through non-profit organizations requires the extension of the analysis not only to governmental but also to non-governmental structures, which are an element of the whole. In this regard, the article provides a historical overview of the development of nonprofit organizations and charitable activities as a large-scale social phenomenon, which made it possible to draw attention to the possibility of using the experience of past years for the purposeful organization of non-state institutions of charity, including by improving social partnerships. Analysis of the current state of non-profit organizations in Ukraine, despite the growth in their number, shows a decrease in the volume of charitable activities. In recent years, the country has taken certain steps to improve charity. However, this is not enough. The institutional environment for philanthropy needs to be improved. The solution to this problem is possible with the active influence of the state on the management of non-commercial activities. Improving the tools of functioning, financing, as well as increasing attention to the development of statistics in this area of activity is considered relevant. In this regard, the purpose of the article is to identify pressing issues and ways to improve charitable organizations. The solution to this problem is possible with the active influence of the state on the management of non-profit activities. The development of the institutional framework of the nonprofit sector of the economy means the improvement of financial reporting, greater openness of charitable organizations, streamlining of their legal relations, liberalized taxation and strengthened control over the activities of non-profit organizations. The article pays special attention to the problem of accumulation and distribution of charitable funds. The potential of charitable organizations can be expanded by shifting the focus of their regulation away from predominantly corporate to regional administration, which increases the importance of the institution of partnership in the development of charity. The article uses historical and logical methods, which allowed to study the formation and development of non-profit organizations in the evolutionary aspect.

scholarly journals A Graph-based Evolutionary Algorithm for Automated Machine Learning

2020 ◽  
Author(s):  
Fei Qi ◽  
Zhaohui Xia ◽  
Gaoyang Tang ◽  
Hang Yang ◽  
Yu Song ◽  
...  
Keyword(s):  
Machine Learning ◽  
Evolutionary Algorithm ◽  
Parameter Optimization ◽  
State Of The Art ◽  
The State ◽  
Complex Structures ◽  
Architecture Evolution ◽  
Automated Machine Learning ◽  
Art Performance

As an emerging field, Automated Machine Learning (AutoML) aims to reduce or eliminate manual operations that require expertise in machine learning. In this paper, a graph-based architecture is employed to represent flexible combinations of ML models, which provides a large searching space compared to tree-based and stacking-based architectures. Based on this, an evolutionary algorithm is proposed to search for the best architecture, where the mutation and heredity operators are the key for architecture evolution. With Bayesian hyper-parameter optimization, the proposed approach can automate the workflow of machine learning. On the PMLB dataset, the proposed approach shows the state-of-the-art performance compared with TPOT, Autostacker, and auto-sklearn. Some of the optimized models are with complex structures which are difficult to obtain in manual design.

scholarly journals A Network Parameter Database False Data Injection Correction Physics-Based Model: A Machine Learning Synthetic Measurement-Based Approach

2021 ◽  
Vol 11 (17) ◽  
pp. 8074
Author(s):  
Tierui Zou ◽  
Nader Aljohani ◽  
Keerthiraj Nagaraj ◽  
Sheng Zou ◽  
Cody Ruben ◽  
...  
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
State Of The Art ◽  
Real Life ◽  
The State ◽  
Wide Area ◽  
Network Parameter ◽  
False Data Injection ◽  
Network Parameters ◽  
Injection Attacks

Concerning power systems, real-time monitoring of cyber–physical security, false data injection attacks on wide-area measurements are of major concern. However, the database of the network parameters is just as crucial to the state estimation process. Maintaining the accuracy of the system model is the other part of the equation, since almost all applications in power systems heavily depend on the state estimator outputs. While much effort has been given to measurements of false data injection attacks, seldom reported work is found on the broad theme of false data injection on the database of network parameters. State-of-the-art physics-based model solutions correct false data injection on network parameter database considering only available wide-area measurements. In addition, deterministic models are used for correction. In this paper, an overdetermined physics-based parameter false data injection correction model is presented. The overdetermined model uses a parameter database correction Jacobian matrix and a Taylor series expansion approximation. The method further applies the concept of synthetic measurements, which refers to measurements that do not exist in the real-life system. A machine learning linear regression-based model for measurement prediction is integrated in the framework through deriving weights for synthetic measurements creation. Validation of the presented model is performed on the IEEE 118-bus system. Numerical results show that the approximation error is lower than the state-of-the-art, while providing robustness to the correction process. Easy-to-implement model on the classical weighted-least-squares solution, highlights real-life implementation potential aspects.

scholarly journals Comparative Quality Estimation for Machine Translation Observations on Machine Learning and Features

2017 ◽  
Vol 108 (1) ◽  
pp. 307-318 ◽  
Author(s):  
Eleftherios Avramidis
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Machine Translation ◽  
State Of The Art ◽  
Linear Method ◽  
The State ◽  
Quality Estimation ◽  
Art Methods ◽  
Improved Performance

AbstractA deeper analysis on Comparative Quality Estimation is presented by extending the state-of-the-art methods with adequacy and grammatical features from other Quality Estimation tasks. The previously used linear method, unable to cope with the augmented features, is replaced with a boosting classifier assisted by feature selection. The methods indicated show improved performance for 6 language pairs, when applied on the output from MT systems developed over 7 years. The improved models compete better with reference-aware metrics.Notable conclusions are reached through the examination of the contribution of the features in the models, whereas it is possible to identify common MT errors that are captured by the features. Many grammatical/fluency features have a good contribution, few adequacy features have some contribution, whereas source complexity features are of no use. The importance of many fluency and adequacy features is language-specific.

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