Results 11 to 20 of about 1,043,746 (297)
Learning from machine learning [PDF]
Journal of Vascular Surgery, 2022 A discussion of the rapidly evolving realm of machine learning.
Ted G. Lewis, Peter J. Denning
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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
Jacob Biamonte +5 more
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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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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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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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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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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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Machine Learning Force Fields [PDF]
Chemical Reviews, 2021 In recent years, the use of Machine Learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational complexity of traditional electronic-structure methods. One of the most promising applications is the construction of ML-based force fields (FFs), with the aim to narrow the gap between the accuracy
Oliver T. Unke +7 more
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Distributional Prototypical Methods for Reliable Explanation Space Construction
IEEE Access, 2023 As deep learning has been successfully deployed in diverse applications, there is an ever increasing need to explain its decision. To explain decisions, case-based reasoning has proved to be effective in many areas.
Hyungjun Joo +3 more
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