214.423 hasil untuk machine learning
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Reproducing kernel methods for machine learning, PDEs, and statistics
…analogues of differential operators and variational tools that connect learning with PDE-style modeling. Part II turns to practice across four domains. In machine learning, we treat supervised and unsupervised tasks, then develop RKHS-based generative modeling, contrasting density and projection…
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Evolving Privacy Protections for Emerging Machine Learning Data Under Carpenter v. United States
…that is constantly being collected by tech companies through the use of machine learning algorithms. Companies like Facebook and Google use machine learning to specifically tailor each user’s experience to their individual preferences. To do so, machine learning algorithms constantly collect…
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Explaining Compassionate Release: A Machine Learning Study of Sentencing Modifications & Judicial Decision Making
…compassionate-release decision making. Until now. Using cutting-edge machine-learning tools in a mixed- methods research design, we built two algorithmic models to analyze the impact of 149 variables on 553 compassionate-release decisions. Our first, “all-inclusive” model determined with…
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Machine-learning media bias
We present an automated method for measuring media bias. Inferring which newspaper published a given article, based only on the frequencies with which it uses different phrases, leads to a conditional probability distribution whose analysis lets us automatically map newspapers and phrases into a…
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Ingredients for Responsible Machine Learning: A Commented Review of The Hitchhiker’s Guide to Responsible Machine Learning
In The hitchhiker’s guide to responsible machine learning, Biecek, Kozak, and Zawada (here BKZ) provide an illustrated and engaging step-by-step guide on how to perform a machine learning (ML) analysis such that the algorithms, the software, and the entire process is interpretable and transparent…
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Can Machine Learning Provide Understanding? How Cosmologists Use Machine Learning to Understand Observations of the Universe
…these observations has become impractical. Simulators have thus turned to machine learning (ML) algorithms instead. Though ML decreases computational expense, one might be worried about the use of ML for scientific investigations: How can algorithms that have repeatedly been described as…
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Explainable Machine Learning
Machine learning methods are widely used in commercial applications and in many scientific areas [...]
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Precision Machine Learning
We explore unique considerations involved in fitting machine learning (ML) models to data with very high precision, as is often required for science applications. We empirically compare various function approximation methods and study how they scale with increasing parameters and data. We find that…
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Machine learning on normalized protein sequences
BACKGROUND: Machine learning techniques have been widely applied to biological sequences, e.g. to predict drug resistance in HIV-1 from sequences of drug target proteins and protein functional classes. As deletions and insertions are frequent in biological sequences, a major limitation of current…
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Machine learning and complex biological data
Machine learning has demonstrated potential in analyzing large, complex biological data. In practice, however, biological information is required in addition to machine learning for successful application.
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Application of Machine Learning in Microbiology
…of high-throughput sequencing technology. To deal with this problem, machine learning (ML) methods have been widely applied to the field of microbiology. Literature reviews have shown that ML can be used in many aspects of microbiology research, especially classification problems, and for exploring…
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Intelligent Machine Learning: Tailor-Making Macromolecules
…the amalgamation of the Kinetic Monte Carlo simulation approach and machine learning techniques. The former, an intelligent modeling tool, is able to model and visualize the intricate inter-relationships of polymerization recipes/conditions (as input variables) and microstructural features of the…
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Learned protein embeddings for machine learning
MOTIVATION: Machine-learning models trained on protein sequences and their measured functions can infer biological properties of unseen sequences without requiring an understanding of the underlying physical or biological mechanisms. Such models enable the prediction and discovery of sequences with…
Juga tersedia di: PubMed Central
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Transforming health policy through machine learning
In their Perspective, Ara Darzi and Hutan Ashrafian give us a tour of the future policymaker's machine learning toolkit.
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Machine Learning in Agriculture: A Review
Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive review of research dedicated to applications of machine…
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Quantum machine learning: a classical perspective
Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning (ML) techniques to impressive results in regression, classification, data generation and reinforcement learning tasks. Despite these successes, the proximity to the physical…
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Thalamocortical dysrhythmia detected by machine learning
…oscillations. We undertook a data-driven approach using support vector machine learning for analyzing resting-state electroencephalography oscillatory patterns in patients with Parkinson’s disease, neuropathic pain, tinnitus, and depression. We show a spectrally equivalent but spatially distinct…
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