Results 11 to 20 of about 2,904,824 (280)
Principled Machine Learning [PDF]
IEEE Journal of Selected Topics in Quantum Electronics, 2022 We introduce the underlying concepts which give rise to some of the commonly used machine learning methods, excluding deep-learning machines and neural networks. We point to their advantages, limitations and potential use in various areas of photonics.
Yordan P. Raykov, David Saad
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Scientific Reports, 2022 Proteins perform many essential functions in biological systems and can be successfully developed as bio-therapeutics. It is invaluable to be able to predict their properties based on a proposed sequence and structure. In this study, we developed a novel
Zichen Wang +10 more
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Mathematics, 2023 Considering the worst-case scenario, the junction-tree algorithm remains the most general solution for exact MAP inference with polynomial run-time guarantees.
Alexander Bauer +2 more
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Ideal Learning Machines* [PDF]
Cognitive Science, 1982 We examine the prospects for finding “best possible” or “ideal” computing machines for various learning tasks. For this purpose, several precise senses of “ideal machine” are considered within the context of formal learning theory. Generally negative results are provided concerning the existence of ideal learning‐machines in the senses considered.
D OSHERSON, M STOB, S WEINSTEIN
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Exploring and Exploiting Conditioning of Reinforcement Learning Agents
IEEE Access, 2020 The outcome of Jacobian singular values regularization was studied for supervised learning problems. In supervised learning settings for linear and nonlinear networks, Jacobian regularization allows for faster learning.
Arip Asadulaev +3 more
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Artificial intelligence and the future of radiographic scoring in rheumatoid arthritis: a viewpoint
Arthritis Research & Therapy, 2022 Rheumatoid arthritis is an autoimmune condition that predominantly affects the synovial joints, causing joint destruction, pain, and disability. Historically, the standard for measuring the long-term efficacy of disease-modifying antirheumatic drugs has ...
Alix Bird +8 more
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F1000Research, 2022 Science is justly admired as a cumulative process (“standing on the shoulders of giants”), yet scientific knowledge is typically built on a patchwork of research contributions without much coordination.
Judith ter Schure, Peter Grünwald
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Machine Learning for Software Engineering: Models, Methods, and Applications [PDF]
, 2018 Machine Learning (ML) is the discipline that studies methods for automatically inferring models from data. Machine learning has been successfully applied in many areas of software engineering ranging from behaviour extraction, to testing, to bug fixing ...
Bennaceur, Amel, Meinke, Karl
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Using Proximity Graph Cut for Fast and Robust Instance-Based Classification in Large Datasets
Complexity, 2021 K-nearest neighbours (kNN) is a very popular instance-based classifier due to its simplicity and good empirical performance. However, large-scale datasets are a big problem for building fast and compact neighbourhood-based classifiers. This work presents
Stanislav Protasov, Adil Mehmood Khan
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Entropy, 2023 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.
Eric J. Michaud, Ziming Liu, Max Tegmark
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