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Showing 20 of 872 academic articles

Nonlinear Flight Control Using Neural Networks

Byoung S. Kim, Anthony J. Calise • Journal of Guidance Control and Dynamics (1997)

The theoretical developmentofa direct adaptivetracking controlarchitectureusingneuralnetworks ispresented. Emphasis is placed on utilization of neural networks in a  ight control architecture based on feedback linearization of the aircraft...

OPENALEX DOI: https://doi.org/10.2514/2.4029

Self-Normalizing Neural Networks

Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter • arXiv (Cornell University) (2017)

Deep Learning has revolutionized vision via convolutional neural networks (CNNs) and natural language processing via recurrent neural networks (RNNs). However, success stories of Deep Learning with standard feed-forward neural networks (FNN...

OPENALEX Open Access DOI: https://doi.org/10.48550/arxiv.1706.02515

Lagrangian Neural Networks

Miles Cranmer, Sam Greydanus, Stephan Hoyer, Peter Battaglia et al. • arXiv (Cornell University) (2020)

Accurate models of the world are built upon notions of its underlying symmetries. In physics, these symmetries correspond to conservation laws, such as for energy and momentum. Yet even though neural network models see increasing use in the...

OPENALEX Open Access DOI: https://doi.org/10.48550/arxiv.2003.04630

Quantum Entanglement in Neural Network States

Dong-Ling Deng, Xiaopeng Li, S. Das Sarma • Physical Review X (2017)

Machine learning has recently gained attention as a possible way to understand phase transitions in many-body quantum systems. A new entanglement analysis reveals crucial properties of the data structures that encode quantum states in a neu...

OPENALEX cc-by DOI: https://doi.org/10.1103/physrevx.7.021021

Spin-glass models of neural networks

Daniel J. Amit, Hanoch Gutfreund, Haim Sompolinsky • Physical Review A (1985)

Two dynamical models, proposed by Hopfield and Little to account for the collective behavior of neural networks, are analyzed. The long-time behavior of these models is governed by the statistical mechanics of infinite-range Ising spin-glas...

OPENALEX DOI: https://doi.org/10.1103/physreva.32.1007

Neural Networks as Tools in Construction

Osama Moselhi, Tarek Hegazy, Paul Fazio • Journal of Construction Engineering and Management (1991)

The competitive and risk‐averse nature of the construction industry and its heuristic problem‐solving needs, among other reasons, have contributed to the development of nontraditional decision‐making tools. Research in artificial intelligen...

OPENALEX DOI: https://doi.org/10.1061/(asce)0733-9364(1991)117:4(606)

Learning and Evolution in Neural Networks

Stefano Nolfi, Domenico Parisi, Jeffrey L. Elman • Adaptive Behavior (1994)

This article describes simulations on populations of neural networks that both evolve at the population level and learn at the individual level. Unlike other simulations, the evolutionary task (finding food in the environment) and the learn...

OPENALEX DOI: https://doi.org/10.1177/105971239400300102

Understanding of a convolutional neural network

Saad Albawi, Tareq Abed Mohammed, Saad Al-Azawi (2017)

The term Deep Learning or Deep Neural Network refers to Artificial Neural Networks (ANN) with multi layers. Over the last few decades, it has been considered to be one of the most powerful tools, and has become very popular in the literatur...

OPENALEX DOI: https://doi.org/10.1109/icengtechnol.2017.8308186