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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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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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Synthetic learning machines [PDF]
BioData Mining, 2014 Using a collection of different terminal nodesize constructed random forests, each generating a synthetic feature, a synthetic random forest is defined as a kind of hyperforest, calculated using the new input synthetic features, along with the original features.Using a large collection of regression and multiclass datasets we show that synthetic random
Ishwaran, Hemant, Malley, James D
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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 vs Human Learning
2021 44th International Convention on Information, Communication and Electronic Technology (MIPRO), 2021 Machine Learning (ML) is a technology to make messages created by humans (text, images, speech etc.) more understandable for computers so that they could better answer humans' queries and needs when recalling this information. Here is considered the ML sub-area - Natural Language Processing (NLP) and presented examples of its methods using text ...
Henno, Jaak +2 more
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Machine Learning Hidden Symmetries
Physical Review Letters, 2022 We present an automated method for finding hidden symmetries, defined as symmetries that become manifest only in a new coordinate system that must be discovered. Its core idea is to quantify asymmetry as violation of certain partial differential equations, and to numerically minimize such violation over the space of all invertible transformations ...
Ziming Liu, Max Tegmark
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IEEE Access, 2020 Colorectal cancer has become one of the most common cause of cancer mortality worldwide, with a five-year survival rate of over 50%. Additionally, the potential of some common polyp types to progress to colorectal cancer is considered high.
Ngoc-Quang Nguyen +2 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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Pharmaceutics, 2023 One of challenges that faces diabetes is the wound healing process. The delayed diabetic wound healing is caused by a complicated molecular mechanism involving numerous physiological variables.
Hala Zuhayri +6 more
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Expert-augmented machine learning [PDF]
Proceedings of the National Academy of Sciences, 2020 Machine learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption is limited by the level of trust afforded by given models. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or
Efstathios D. Gennatas +13 more
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