scholarly journals KG-COVID-19: a framework to produce customized knowledge graphs for COVID-19 response

2020 ◽  
Author(s):  
Justin Reese ◽  
Deepak Unni ◽  
Tiffany J. Callahan ◽  
Luca Cappelletti ◽  
Vida Ravanmehr ◽  
...  
Keyword(s):  
Machine Learning ◽  
User Interface ◽  
Learning Task ◽  
Research Community ◽  
Biological Knowledge ◽  
Biomedical Data ◽  
Effective Response ◽  
Learning Tasks ◽  
Flexible Framework ◽  
Knowledge Graphs

SUMMARYIntegrated, up-to-date data about SARS-CoV-2 and coronavirus disease 2019 (COVID-19) is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this knowledge is difficult and time consuming, since much of it is in siloed databases or in textual format. Furthermore, the data required by the research community varies drastically for different tasks - the optimal data for a machine learning task, for example, is much different from the data used to populate a browsable user interface for clinicians. To address these challenges, we created KG-COVID-19, a flexible framework that ingests and integrates biomedical data to produce knowledge graphs (KGs) for COVID-19 response. This KG framework can also be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics.BIGGER PICTUREAn effective response to the COVID-19 pandemic relies on integration of many different types of data available about SARS-CoV-2 and related viruses. KG-COVID-19 is a framework for producing knowledge graphs that can be customized for downstream applications including machine learning tasks, hypothesis-based querying, and browsable user interface to enable researchers to explore COVID-19 data and discover relationships.

Ensemble Learning for Regression

2011 ◽  
pp. 777-782
Author(s):  
Niall Rooney
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Neural Networks ◽  
Ensemble Learning ◽  
Learning Task ◽  
General Definition ◽  
Learning Tasks ◽  
Continuous Output ◽  
Input Variables ◽  
The Given

The concept of ensemble learning has its origins in research from the late 1980s/early 1990s into combining a number of artificial neural networks (ANNs) models for regression tasks. Ensemble learning is now a widely deployed and researched topic within the area of machine learning and data mining. Ensemble learning, as a general definition, refers to the concept of being able to apply more than one learning model to a particular machine learning problem using some method of integration. The desired goal of course is that the ensemble as a unit will outperform any of its individual members for the given learning task. Ensemble learning has been extended to cover other learning tasks such as classification (refer to Kuncheva, 2004 for a detailed overview of this area), online learning (Fern & Givan, 2003) and clustering (Strehl & Ghosh, 2003). The focus of this article is to review ensemble learning with respect to regression, where by regression, we refer to the supervised learning task of creating a model that relates a continuous output variable to a vector of input variables.

Handling Local Patterns in Collaborative Structuring

2008 ◽  
pp. 167-186
Author(s):  
Ingo Mierswa ◽  
Katharina Morik ◽  
Michael Wurst
Keyword(s):  
Machine Learning ◽  
Learning Task ◽  
Media Management ◽  
Cluster Ensembles ◽  
Learning Tasks ◽  
New Learning ◽  
Local Patterns ◽  
Uniform Manner ◽  
Personal Media ◽  
Private Collections

Media collections in the internet have become a commercial success and the structuring of large media collections has thus become an issue. Personal media collections are locally structured in very different ways by different users. The level of detail, the chosen categories, and the extensions can differ completely from user to user. Can machine learning be of help also for structuring personal collections? Since users do not want to have their hand-made structures overwritten, one could deny the benefit of automatic structuring. We argue that what seems to exclude machine learning, actually poses a new learning task. We propose a notation which allows us to describe machine learning tasks in a uniform manner. Keeping the demands of structuring private collections in mind, we define the new learning task of localized alternative cluster ensembles. An algorithm solving the new task is presented together with its application to distributed media management.

scholarly journals Knowledge-Based Biomedical Data Science

2020 ◽  
Vol 3 (1) ◽  
pp. 23-41 ◽  
Author(s):  
Tiffany J. Callahan ◽  
Ignacio J. Tripodi ◽  
Harrison Pielke-Lombardo ◽  
Lawrence E. Hunter
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Data Science ◽  
Recent Progress ◽  
Computer Systems ◽  
Biomedical Data ◽  
Design And Implementation ◽  
Knowledge Based ◽  
Address Data ◽  
Knowledge Graphs

Knowledge-based biomedical data science involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey recent progress in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as progress on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing to construct knowledge graphs, and the expansion of novel knowledge-based approaches to clinical and biological domains.

scholarly journals Universal discriminative quantum neural networks

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
H. Chen ◽  
L. Wossnig ◽  
S. Severini ◽  
H. Neven ◽  
M. Mohseni
Keyword(s):  
Machine Learning ◽  
Hybrid Methods ◽  
Quantum Circuit ◽  
Learning Task ◽  
Error Rates ◽  
Quantum Circuits ◽  
Circuit Optimization ◽  
Learning Tasks ◽  
Classical Analogue ◽  
Optimal Values

AbstractRecent results have demonstrated the successful applications of quantum-classical hybrid methods to train quantum circuits for a variety of machine learning tasks. A natural question to ask is consequentially whether we can also train such quantum circuits to discriminate quantum data, i.e., perform classification on data stored in form of quantum states. Although quantum mechanics fundamentally forbids deterministic discrimination of non-orthogonal states, we show in this work that it is possible to train a quantum circuit to discriminate such data with a trade-off between minimizing error rates and inconclusiveness rates of the classification tasks. Our approach achieves at the same time a performance which is close to the theoretically optimal values and a generalization ability to previously unseen quantum data. This generalization power hence distinguishes our work from previous circuit optimization results and furthermore provides an example of a quantum machine learning task that has inherently no classical analogue.

scholarly journals Spectral bias and task-model alignment explain generalization in kernel regression and infinitely wide neural networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Abdulkadir Canatar ◽  
Blake Bordelon ◽  
Cengiz Pehlevan
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Kernel Regression ◽  
Learning Task ◽  
Learning Curves ◽  
Generalization Error ◽  
Theoretical Understanding ◽  
Classical Statistics ◽  
Deep Networks ◽  
Model Alignment

AbstractA theoretical understanding of generalization remains an open problem for many machine learning models, including deep networks where overparameterization leads to better performance, contradicting the conventional wisdom from classical statistics. Here, we investigate generalization error for kernel regression, which, besides being a popular machine learning method, also describes certain infinitely overparameterized neural networks. We use techniques from statistical mechanics to derive an analytical expression for generalization error applicable to any kernel and data distribution. We present applications of our theory to real and synthetic datasets, and for many kernels including those that arise from training deep networks in the infinite-width limit. We elucidate an inductive bias of kernel regression to explain data with simple functions, characterize whether a kernel is compatible with a learning task, and show that more data may impair generalization when noisy or not expressible by the kernel, leading to non-monotonic learning curves with possibly many peaks.

scholarly journals TPOT-NN: augmenting tree-based automated machine learning with neural network estimators

2021 ◽  
Author(s):  
Joseph D. Romano ◽  
Trang T. Le ◽  
Weixuan Fu ◽  
Jason H. Moore
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Binary Classification ◽  
Inductive Learning ◽  
Future Directions ◽  
High Performing ◽  
Learning Tasks ◽  
Benchmark Datasets ◽  
Automated Machine Learning ◽  
Standard Tree

AbstractAutomated machine learning (AutoML) and artificial neural networks (ANNs) have revolutionized the field of artificial intelligence by yielding incredibly high-performing models to solve a myriad of inductive learning tasks. In spite of their successes, little guidance exists on when to use one versus the other. Furthermore, relatively few tools exist that allow the integration of both AutoML and ANNs in the same analysis to yield results combining both of their strengths. Here, we present TPOT-NN—a new extension to the tree-based AutoML software TPOT—and use it to explore the behavior of automated machine learning augmented with neural network estimators (AutoML+NN), particularly when compared to non-NN AutoML in the context of simple binary classification on a number of public benchmark datasets. Our observations suggest that TPOT-NN is an effective tool that achieves greater classification accuracy than standard tree-based AutoML on some datasets, with no loss in accuracy on others. We also provide preliminary guidelines for performing AutoML+NN analyses, and recommend possible future directions for AutoML+NN methods research, especially in the context of TPOT.

scholarly journals Representing Deep Neural Networks Latent Space Geometries with Graphs

Algorithms ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 39
Author(s):  
Carlos Lassance ◽  
Vincent Gripon ◽  
Antonio Ortega
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Objective Function ◽  
Learning Process ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
The Core ◽  
Learning Tasks ◽  
Latent Space

Deep Learning (DL) has attracted a lot of attention for its ability to reach state-of-the-art performance in many machine learning tasks. The core principle of DL methods consists of training composite architectures in an end-to-end fashion, where inputs are associated with outputs trained to optimize an objective function. Because of their compositional nature, DL architectures naturally exhibit several intermediate representations of the inputs, which belong to so-called latent spaces. When treated individually, these intermediate representations are most of the time unconstrained during the learning process, as it is unclear which properties should be favored. However, when processing a batch of inputs concurrently, the corresponding set of intermediate representations exhibit relations (what we call a geometry) on which desired properties can be sought. In this work, we show that it is possible to introduce constraints on these latent geometries to address various problems. In more detail, we propose to represent geometries by constructing similarity graphs from the intermediate representations obtained when processing a batch of inputs. By constraining these Latent Geometry Graphs (LGGs), we address the three following problems: (i) reproducing the behavior of a teacher architecture is achieved by mimicking its geometry, (ii) designing efficient embeddings for classification is achieved by targeting specific geometries, and (iii) robustness to deviations on inputs is achieved via enforcing smooth variation of geometry between consecutive latent spaces. Using standard vision benchmarks, we demonstrate the ability of the proposed geometry-based methods in solving the considered problems.

scholarly journals An Empirical Comparison of Bias Reduction Methods on Real-World Problems in High-Stakes Policy Settings

2021 ◽  
Vol 23 (1) ◽  
pp. 69-85
Author(s):  
Hemank Lamba ◽  
Kit T. Rodolfa ◽  
Rayid Ghani
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Empirical Work ◽  
Bias Reduction ◽  
Research Community ◽  
High Stakes ◽  
Social Good ◽  
Social Service Delivery ◽  
Reduction Methods ◽  
Applications Of Machine Learning

Applications of machine learning (ML) to high-stakes policy settings - such as education, criminal justice, healthcare, and social service delivery - have grown rapidly in recent years, sparking important conversations about how to ensure fair outcomes from these systems. The machine learning research community has responded to this challenge with a wide array of proposed fairness-enhancing strategies for ML models, but despite the large number of methods that have been developed, little empirical work exists evaluating these methods in real-world settings. Here, we seek to fill this research gap by investigating the performance of several methods that operate at different points in the ML pipeline across four real-world public policy and social good problems. Across these problems, we find a wide degree of variability and inconsistency in the ability of many of these methods to improve model fairness, but postprocessing by choosing group-specific score thresholds consistently removes disparities, with important implications for both the ML research community and practitioners deploying machine learning to inform consequential policy decisions.

A content spectral-based text representation

2021 ◽  
pp. 1-12
Author(s):  
Melesio Crespo-Sanchez ◽  
Ivan Lopez-Arevalo ◽  
Edwin Aldana-Bobadilla ◽  
Alejandro Molina-Villegas
Keyword(s):  
Machine Learning ◽  
Text Analysis ◽  
Question Answering ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Text Representation ◽  
Feature Vectors ◽  
Learning Tasks ◽  
Semantic Component ◽  
Vector Representations

In the last few years, text analysis has grown as a keystone in several domains for solving many real-world problems, such as machine translation, spam detection, and question answering, to mention a few. Many of these tasks can be approached by means of machine learning algorithms. Most of these algorithms take as input a transformation of the text in the form of feature vectors containing an abstraction of the content. Most of recent vector representations focus on the semantic component of text, however, we consider that also taking into account the lexical and syntactic components the abstraction of content could be beneficial for learning tasks. In this work, we propose a content spectral-based text representation applicable to machine learning algorithms for text analysis. This representation integrates the spectra from the lexical, syntactic, and semantic components of text producing an abstract image, which can also be treated by both, text and image learning algorithms. These components came from feature vectors of text. For demonstrating the goodness of our proposal, this was tested on text classification and complexity reading score prediction tasks obtaining promising results.

scholarly journals A REVIEW OF FEATURE EXTRACTION METHODS ON MACHINE LEARNING

2021 ◽  
Vol 6 (22) ◽  
pp. 51-59
Author(s):  
Mustazzihim Suhaidi ◽  
Rabiah Abdul Kadir ◽  
Sabrina Tiun
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Feature Selection ◽  
Input Data ◽  
Feature Vector ◽  
Learning Algorithm ◽  
Extraction Methods ◽  
Machine Learning Algorithm ◽  
Learning Tasks ◽  
Low Dimensional

Extracting features from input data is vital for successful classification and machine learning tasks. Classification is the process of declaring an object into one of the predefined categories. Many different feature selection and feature extraction methods exist, and they are being widely used. Feature extraction, obviously, is a transformation of large input data into a low dimensional feature vector, which is an input to classification or a machine learning algorithm. The task of feature extraction has major challenges, which will be discussed in this paper. The challenge is to learn and extract knowledge from text datasets to make correct decisions. The objective of this paper is to give an overview of methods used in feature extraction for various applications, with a dataset containing a collection of texts taken from social media.

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