Results 41 to 50 of about 8,972,433 (325)
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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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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Pattern Recognition and Machine Learning
J. Electronic Imaging, 2006 Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous ...
Radford M. Neal
semanticscholar +1 more source
Interpretable Machine Learning
Hands-On Machine Learning with R, 2019 Interpretable machine learning has become a popular research direction as deep neural networks (DNNs) have become more powerful and their applications more mainstream, yet DNNs remain difficult to understand. Testing with Concept Activation Vectors, TCAV,
Bradley C. Boehmke, Brandon M. Greenwell
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Multimodal Machine Learning: A Survey and Taxonomy [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017 Our experience of the world is multimodal - we see objects, hear sounds, feel texture, smell odors, and taste flavors. Modality refers to the way in which something happens or is experienced and a research problem is characterized as multimodal when it ...
T. Baltrušaitis +2 more
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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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Machine learning in bioinformatics [PDF]
Briefings in Bioinformatics, 2006 This article reviews machine learning methods for bioinformatics. It presents modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochastic heuristics for optimization.
Larrañaga Múgica, Pedro María +10 more
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Teleconnection Patterns of Different El Niño Types Revealed by Climate Network Curvature
Geophysical Research Letters, 2022 The diversity of El Niño events is commonly described by two distinct flavors, the Eastern Pacific (EP) and Central Pacific (CP) type. While the remote impacts, that is, teleconnections, of EP and CP events have been studied for different regions ...
Felix M. Strnad +3 more
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Towards Modular Machine Learning Solution Development: Benefits and Trade-offs [PDF]
arXiv, 2023 Machine learning technologies have demonstrated immense capabilities in various domains. They play a key role in the success of modern businesses. However, adoption of machine learning technologies has a lot of untouched potential. Cost of developing custom machine learning solutions that solve unique business problems is a major inhibitor to far ...
arxiv
Physics Letters B, 2020 We use machine learning techniques to solve the nuclear two-body bound state problem, the deuteron. We use a minimal one-layer, feed-forward neural network to represent the deuteron S- and D-state wavefunction in momentum space, and solve the problem variationally using ready-made machine learning tools.
J.W.T. Keeble, A. Rios
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