Results 21 to 30 of about 3,002,307 (310)
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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Supervised Learning in Physical Networks: From Machine Learning to Learning Machines [PDF]
Physical Review X, 2021 18 pages, 9 ...
Menachem Stern +3 more
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Quantum machine learning: a classical perspective [PDF]
, 2018 Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning techniques to impressive results in regression, classification, data-generation and reinforcement learning tasks.
Ben-David S +15 more
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Physical Review D, 2022 We propose a simple method to identify a continuous Lie algebra symmetry in a dataset through regression by an artificial neural network. Our proposal takes advantage of the $ \mathcal{O}(ε^2)$ scaling of the output variable under infinitesimal symmetry transformations on the input variables. As symmetry transformations are generated post-training, the
Sean Craven +3 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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Industry-scale application and evaluation of deep learning for drug target prediction [PDF]
, 2019 Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling.
Ashby, Thomas J. +18 more
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Machine Learning for Communications [PDF]
Entropy, 2021 Due to the proliferation of applications and services that run over communication networks, ranging from video streaming and data analytics to robotics and augmented reality, tomorrow’s networks will be faced with increasing challenges resulting from the explosive growth of data traffic demand with significantly varying performance requirements [...]
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Power Allocation Schemes Based on Deep Learning for Distributed Antenna Systems
IEEE Access, 2020 In recent years, a lot of power allocation algorithms have been proposed to maximize spectral efficiency (SE) and energy efficiency (EE) for the distributed antenna systems (DAS).
Gongbin Qian +4 more
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Machine Learned Learning Machines
, 2017 There are two common approaches for optimizing the performance of a machine: genetic algorithms and machine learning. A genetic algorithm is applied over many generations whereas machine learning works by applying feedback until the system meets a performance threshold. Though these are methods that typically operate separately, we combine evolutionary
Sheneman, Leigh, Hintze, Arend
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Quantum-chemical insights from deep tensor neural networks
Nature Communications, 2017 Machine learning is an increasingly popular approach to analyse data and make predictions. Here the authors develop a ‘deep learning’ framework for quantitative predictions and qualitative understanding of quantum-mechanical observables of chemical ...
Kristof T. Schütt +4 more
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