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Weak Supervision
Weak supervision is a branch of machine learning where noisy, limited, or imprecise sources are used to provide supervision signal for labeling large amounts of training data in a supervised learning setting. This approach alleviates the burden of obtaining hand-labeled data sets, which can be costly or impractical. Instead, inexpensive weak labels are employed with the understanding that they are imperfect, but can nonetheless be used to create a strong predictive model. Problem of labeled training data Machine learning models and techniques are increasingly accessible to researchers and developers; the real-world usefulness of these models, however, depends on access to high-quality labeled training data. This need for labeled training data often proves to be a significant obstacle to the application of machine learning models within an organization or industry. This bottleneck effect manifests itself in various ways, including the following examples: Insufficient quantity of la ...
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Machine Learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.Hu, J.; Niu, H.; Carrasco, J.; Lennox, B.; Arvin, F.,Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning IEEE Transactions on Vehicular Technology, 2020. A subset of machine learning is closely related to computational statistics, which focuses on making predicti ...
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Domain Adaptation
Domain adaptation is a field associated with machine learning and transfer learning. This scenario arises when we aim at learning from a source data distribution a well performing model on a different (but related) target data distribution. For instance, one of the tasks of the common spam filtering problem consists in adapting a model from one user (the source distribution) to a new user who receives significantly different emails (the target distribution). Domain adaptation has also been shown to be beneficial for learning unrelated sources. Note that, when more than one source distribution is available the problem is referred to as multi-source domain adaptation. Overview Domain adaptation is the ability to apply an algorithm trained in one or more "source domains" to a different (but related) "target domain". Domain adaptation is a subcategory of transfer learning. In domain adaptation, the source and target domains all have the same feature space (but different distribut ...
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Unsupervised Learning
Unsupervised learning is a type of algorithm that learns patterns from untagged data. The hope is that through mimicry, which is an important mode of learning in people, the machine is forced to build a concise representation of its world and then generate imaginative content from it. In contrast to supervised learning where data is tagged by an expert, e.g. tagged as a "ball" or "fish", unsupervised methods exhibit self-organization that captures patterns as probability densities or a combination of neural feature preferences encoded in the machine's weights and activations. The other levels in the supervision spectrum are reinforcement learning where the machine is given only a numerical performance score as guidance, and semi-supervised learning where a small portion of the data is tagged. Neural networks Tasks vs. methods Neural network tasks are often categorized as discriminative (recognition) or generative (imagination). Often but not always, discriminative tas ...
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Labeled Data
Labeled data is a group of samples that have been tagged with one or more labels. Labeling typically takes a set of unlabeled data and augments each piece of it with informative tags. For example, a data label might indicate whether a photo contains a horse or a cow, which words were uttered in an audio recording, what type of action is being performed in a video, what the topic of a news article is, what the overall sentiment of a tweet is, or whether a dot in an X-ray is a tumor. Labels can be obtained by asking humans to make judgments about a given piece of unlabeled data. Labeled data is significantly more expensive to obtain than the raw unlabeled data. Crowdsourced labeled data In 2006 Fei-Fei Li, the co-director of the Stanford Human-Centered AI Institute, set out to improve the artificial intelligence models and algorithms for image recognition by significantly enlarging the training data. The researchers downloaded millions of images from the World Wide Web and a team o ...
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Example Of Unlabeled Data In Semisupervised Learning
Example may refer to: * '' exempli gratia'' (e.g.), usually read out in English as "for example" * .example, reserved as a domain name that may not be installed as a top-level domain of the Internet ** example.com, example.net, example.org, example.edu, second-level domain names reserved for use in documentation as examples * HMS ''Example'' (P165), an Archer-class patrol and training vessel of the Royal Navy Arts * ''The Example'', a 1634 play by James Shirley * ''The Example'' (comics), a 2009 graphic novel by Tom Taylor and Colin Wilson * Example (musician), the British dance musician Elliot John Gleave (born 1982) * ''Example'' (album), a 1995 album by American rock band For Squirrels See also * * Exemplar (other), a prototype or model which others can use to understand a topic better * Exemplum, medieval collections of short stories to be told in sermons * Eixample The Eixample (; ) is a district of Barcelona between the old city ( Ciutat Vella) an ...
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University Of Alberta Faculty Of Engineering
The University of Alberta Faculty of Engineering is one of the largest engineering schools in Canada in terms of size, international impact, and reputation. The faculty is home to 1 Canada Excellence Research Chair, 16 Canada Research Chairs, 13 Natural Sciences and Engineering Research Council chairs, and 5 Foundation Supported Chairs. History Engineering classes have been taught at the University of Alberta since it opened in 1908. Programs Undergraduate The undergraduate bachelor's degree program for engineering is based on a 4-5 year program. The traditional program involves taking two terms (fall and winter) for four years to complete a degree. The co-op program involves intermittent work placement terms, within a 5-year period. Co-op programs are usually in higher demand by students than the 4 year "Traditional" programs, and thus the entrance GPA tends to be higher. New students to engineering must first take two terms of general engineering courses before speciali ...
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Johns Hopkins University
Johns Hopkins University (Johns Hopkins, Hopkins, or JHU) is a private university, private research university in Baltimore, Maryland. Founded in 1876, Johns Hopkins is the oldest research university in the United States and in the western hemisphere. It consistently ranks among the most prestigious universities in the United States and the world. The university was named for its first benefactor, the American entrepreneur and Quaker philanthropist Johns Hopkins. Hopkins' $7 million bequest to establish the university was the largest Philanthropy, philanthropic gift in U.S. history up to that time. Daniel Coit Gilman, who was inaugurated as :Presidents of Johns Hopkins University, Johns Hopkins's first president on February 22, 1876, led the university to revolutionize higher education in the U.S. by integrating teaching and research. In 1900, Johns Hopkins became a founding member of the American Association of Universities. The university has led all Higher education in the U ...
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University Of Massachusetts Amherst
The University of Massachusetts Amherst (UMass Amherst, UMass) is a public research university in Amherst, Massachusetts and the sole public land-grant university in Commonwealth of Massachusetts. Founded in 1863 as an agricultural college, it is the flagship and the largest campus in the University of Massachusetts system, as well as the first established. It is also a member of the Five College Consortium, along with four other colleges in the Pioneer Valley: Amherst College, Smith College, Mount Holyoke College, and Hampshire College. As of Fall 2022, UMass Amherst has an annual enrollment of more than 32,000 students, along with approximately 1,900 faculty members. It is the largest university in Massachusetts by campus size and second largest university by enrollment in Massachusetts, after Boston University. The university offers academic degrees in 109 undergraduate, 77 master's and 48 doctoral programs. Programs are coordinated in nine schools and colleges. The Universit ...
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GitHub
GitHub, Inc. () is an Internet hosting service for software development and version control using Git. It provides the distributed version control of Git plus access control, bug tracking, software feature requests, task management, continuous integration, and wikis for every project. Headquartered in California, it has been a subsidiary of Microsoft since 2018. It is commonly used to host open source software development projects. As of June 2022, GitHub reported having over 83 million developers and more than 200 million repositories, including at least 28 million public repositories. It is the largest source code host . History GitHub.com Development of the GitHub.com platform began on October 19, 2007. The site was launched in April 2008 by Tom Preston-Werner, Chris Wanstrath, P. J. Hyett and Scott Chacon after it had been made available for a few months prior as a beta release. GitHub has an annual keynote called GitHub Universe. Organizational ...
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Confident Learning
Confidence is a state of being clear-headed either that a hypothesis or prediction is correct or that a chosen course of action is the best or most effective. Confidence comes from a Latin word 'fidere' which means "to trust"; therefore, having self-confidence is having trust in one's self. Arrogance or hubris, in comparison, is the state of having unmerited confidence—believing something or someone is correct or capable when evidence or reasons for this belief are lacking. Overconfidence or presumptuousness is excessive belief in someone (or something) succeeding, without any regard for failure. Confidence can be a self-fulfilling prophecy as those without it may fail or not try because they lack it and those with it may succeed because they have it rather than because of an innate ability. The concept of self-confidence is commonly defined as self-assurance in one's personal judgment, ability, power, etc. One's self-confidence increases as a result of experiences of hav ...
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Deep Learning
Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. Deep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks, convolutional neural networks and Transformers have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, Climatology, climate science, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance. Artificial neural networks (ANNs) were inspired by information processing and distributed communication nodes in biological systems. ANNs have various differences from biological brains. Specifically, artificial ...
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Python (programming Language)
Python is a high-level, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation. Python is dynamically-typed and garbage-collected. It supports multiple programming paradigms, including structured (particularly procedural), object-oriented and functional programming. It is often described as a "batteries included" language due to its comprehensive standard library. Guido van Rossum began working on Python in the late 1980s as a successor to the ABC programming language and first released it in 1991 as Python 0.9.0. Python 2.0 was released in 2000 and introduced new features such as list comprehensions, cycle-detecting garbage collection, reference counting, and Unicode support. Python 3.0, released in 2008, was a major revision that is not completely backward-compatible with earlier versions. Python 2 was discontinued with version 2.7.18 in 2020. Python consistently ranks as ...
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