Foundations Of Machine Learning Research Articles

Automated pipeline for superalloy data by text mining

Data provides a foundation for machine learning, which has accelerated data-driven materials design. The scientific literature contains a large amount of high-quality, reliable data, and automatically extracting data from the literature continues to be a challenge. We propose a natural language processing pipeline to capture both chemical composition and property data that allows analysis and prediction of superalloys. Within 3 h, 2531 records with both composition and property are extracted from 14,425 articles, covering γ′ solvus temperature, density, solidus, and liquidus temperatures. A data-driven model for γ′ solvus temperature is built to predict unexplored Co-based superalloys with high γ′ solvus temperatures within a relative error of 0.81%. We test the predictions via synthesis and characterization of three alloys. A web-based toolkit as an online open-source platform is provided and expected to serve as the basis for a general method to search for targeted materials using data extracted from the literature.

Towards an Adaptive Educational Course on the Mathematical Foundations of Machine Learning

Today the development of information technology is closely related to the creation and application of machine learning and data analysis methods. In this regard, the need for training specialists in this area is growing. Very often, the study of machine learning methods is combined with the study of a certain programming language and the tools of its specialized library. This approach is undoubtedly justified, because it provides the possibility of accelerated application of the knowledge gained in practice. At the same time, it should be noted that with this approach, it is rather not machine learning methods that are studied, but a certain set of methodological techniques for using the tools of the specialized library. The presented work is devoted to the experience of creating an adaptive educational course on the mathematical foundations of machine learning. This course is aimed at undergraduate and graduate students of mathematical specialties. It is divided into core and variable parts. The obligatory core part is built around the PAC learning model and the binary classification problem. Within the variable part, issues of the weak learning model and the boosting methods are considered. Also a methodology of changing the variable part of the course is discussed.

Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar: Foundations of machine learning, second edition

Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar: Foundations of machine learning, second edition

Foundations of Machine Learning

The emphasis of machine learning is on automatic methods. In other words, the goal is to devise learning algorithms that do the learning automatically without human intervention or assistance. The machine learning paradigm can be viewed as programming by example. Often we have a specific task in mind, such as filtering. But rather than program the computer to solve the task directly, in machine learning, we seek methods by which the computer will come up with its own program based on examples that i provide. Before getting to our first learning model, i will need some definitions. An example (sometimes also called an instance) is the object that is being classified. For instance, in filtering, the email messages are the examples. Usually, an example is described by a set of attributes, also known as features or variables. For instance, in medical diagnosis, a patient might be described by attributes such as gender, age, weight, blood pressure, body temperature, etc. The label is the category that we are trying to predict. For instance, in filtering, the possible labels are spam and not spam. During training, the learning algorithm is supplied with labeled examples, while during testing, only unlabeled examples are provided.

Application of Lean principles and software solutions for maintenance records in continuing airworthiness management organisations

ABSTRACTThis paper discusses how the processes of Continuing Airworthiness Management Organisations can be made more efficient by applying Lean principles and digital solutions, to obtain costs savings and improved compliance standards. Lean management is the process of eliminating waste by maximising value through balancing workloads and cutting out inefficient processes. Though Lean was initially applied to manufacturing production lines, an opportunity exists to apply the principles of Lean in airworthiness management. One such aspect of airworthiness management is the processing of records which is an essential part of safety and compliance. Software solutions designed with Lean philosophy and focus on airworthiness management processes can provide benefits in managing the flow of records and extracting actionable information from them for cost reduction and improving safety. Further value can be extracted by applying additional software solutions that will lay a strong foundation for machine learning and predictive analytics – taking aircraft maintenance from a reactive model towards a proactive model.

Incrementality Bidding & Attribution

The causal effect of showing an ad to a potential customer versus not, commonly referred to as “incrementality,” is the fundamental question of advertising effectiveness. In digital advertising three major puzzle pieces are central to rigorously quantifying advertising incrementality: ad buying/bidding/pricing, attribution, and experimentation. Building on the foundations of machine learning and causal econometrics, we propose a methodology that unifies these three concepts into a computationally viable model of both bidding and attribution which spans the randomization, training, cross validation, scoring, and conversion attribution of advertising’s causal effects. Implementation of this approach is likely to secure a significant improvement in the return on investment of advertising.

Simulating a perceptron on a quantum computer

Perceptrons are the basic computational unit of artificial neural networks, as they model the activation mechanism of an output neuron due to incoming signals from its neighbours. As linear classifiers, they play an important role in the foundations of machine learning. In the context of the emerging field of quantum machine learning, several attempts have been made to develop a corresponding unit using quantum information theory. Based on the quantum phase estimation algorithm, this paper introduces a quantum perceptron model imitating the step-activation function of a classical perceptron. This scheme requires resources in $\mathcal{O}(n)$ (where $n$ is the size of the input) and promises efficient applications for more complex structures such as trainable quantum neural networks.

A foundation for machine learning in design

This paper presents a formalism for considering the issues of learning in design. A foundation for machine learning in design (MLinD) is defined so as to provide answers to basic questions on learning in design, such as, “What types of knowledge can be learnt?”, “How does learning occur?”, and “When does learning occur?”. Five main elements of MLinD are presented as the input knowledge, knowledge transformers, output knowledge, goals/reasons for learning, and learning triggers. Using this foundation, published systems in MLinD were reviewed. The systematic review presents a basis for validating the presented foundation. The paper concludes that there is considerable work to be carried out in order to fully formalize the foundation of MLinD.

Hierarchical feature extraction and representation in the neocognitron: [formula omitted], Hughes Aircraft Company, 8433 Fallbrook Avenue, Bldg 262, MS C64, Canoga Park, CA 91304

Perceptrons are the basic computational unit of artificial neural networks, as they model the activation mechanism of an output neuron due to incoming signals from its neighbours. As linear classifiers, they play an important role in the foundations of machine learning. In the context of the emerging field of quantum machine learning, several attempts have been made to develop a corresponding unit using quantum information theory. Based on the quantum phase estimation algorithm, this paper introduces a quantum perceptron model imitating the step-activation function of a classical perceptron. This scheme requires resources in O(n) (where n is the size of the input) and promises efficient applications for more complex structures such as trainable quantum neural networks.