Results 11 to 20 of about 9,750,100 (244)
Machine learning and deep learning [PDF]
Electronic Markets, 2021 AbstractToday, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model building and solve associated tasks.
Christian Janiesch +2 more
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Quantum machine learning [PDF]
Nature, 2017 Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Since quantum systems produce counter-intuitive patterns believed not to be efficiently produced by classical systems, it is reasonable to postulate that quantum computers may outperform classical computers
Seth Lloyd +6 more
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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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Practical Black-Box Attacks against Machine Learning [PDF]
ACM Asia Conference on Computer and Communications Security, 2016 Machine learning (ML) models, e.g., deep neural networks (DNNs), are vulnerable to adversarial examples: malicious inputs modified to yield erroneous model outputs, while appearing unmodified to human observers. Potential attacks include having malicious
Nicolas Papernot +5 more
semanticscholar +1 more source
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
doaj +1 more source
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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Optimization Methods for Large-Scale Machine Learning [PDF]
SIAM Review, 2016 This paper provides a review and commentary on the past, present, and future of numerical optimization algorithms in the context of machine learning applications.
L. Bottou, Frank E. Curtis, J. Nocedal
semanticscholar +1 more source
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 in the Air [PDF]
IEEE Journal on Selected Areas in Communications, 2019 Thanks to the recent advances in processing speed and data acquisition and storage, machine learning (ML) is penetrating every facet of our lives, and transforming research in many areas in a fundamental manner. Wireless communications is another success story -- ubiquitous in our lives, from handheld devices to wearables, smart homes, and automobiles.
D. Gunduz +5 more
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