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Understanding transformation tolerant visual object representations in the human brain and convolutional neural networks

Yaoda Xu, Maryam Vaziri-Pashkam • NeuroImage (2022)

Forming transformation-tolerant object representations is critical to high-level primate vision. Despite its significance, many details of tolerance in the human brain remain unknown. Likewise, despite the ability of convolutional neural ne...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.neuroimage.2022.119635

An Introduction to Convolutional Neural Networks

Keiron O’Shea, Ryan Nash • arXiv (Cornell University) (2015)

The field of machine learning has taken a dramatic twist in recent times, with the rise of the Artificial Neural Network (ANN). These biologically inspired computational models are able to far exceed the performance of previous forms of art...

OPENALEX Open Access DOI: https://doi.org/10.48550/arxiv.1511.08458

On Calibration of Modern Neural Networks

Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger • arXiv (Cornell University) (2017)

Confidence calibration -- the problem of predicting probability estimates representative of the true correctness likelihood -- is important for classification models in many applications. We discover that modern neural networks, unlike thos...

OPENALEX Open Access DOI: https://doi.org/10.48550/arxiv.1706.04599

Tailoring Mission Effectiveness and Efficiency of a Ground Vehicle Using Exergy-Based Model Predictive Control (MPC)

Robert Jane, Tae Young Kim, Emily Glass, Emilee Mossman et al. • Energies (2021)

To ensure dominance over a multi-domain battlespace, energy and power utilization must be accurately characterized for the dissimilar operational conditions. Using MATLAB/Simulink in combination with multiple neural networks, we created a m...

DOAJ Open Access (DOAJ) DOI: 10.3390/en14196049

A Comprehensive Analysis of Multilayer Community Detection Algorithms for Application to EEG-Based Brain Networks

Maria Grazia Puxeddu, Maria Grazia Puxeddu, Manuela Petti, Manuela Petti et al. • Frontiers in Systems Neuroscience (2021)

Modular organization is an emergent property of brain networks, responsible for shaping communication processes and underpinning brain functioning. Moreover, brain networks are intrinsically multilayer since their attributes can vary across...

DOAJ Open Access (DOAJ) DOI: 10.3389/fnsys.2021.624183

Monthly Evapotranspiration Modeling in the Absence of Climatic Data Using the Soft Computing Methods in West and Northwest of Iran

Saman Maroufpour, Eisa Maroufpoor, Hadi Sanikhani • مهندسی منابع آب (2017)

Evapotranspiration (ET0), a major component of the hydrologic cycle, is important in water resources development and irrigation planning. The ET0 for west and northwest of Iran was estimated using the FAO Penman-Montieth method (FAO-56) as ...

DOAJ Open Access (DOAJ)

Fallback Variable History NNLMs: Efficient NNLMs by precomputation and stochastic training.

Francisco J Zamora-Martínez, Salvador España-Boquera, Maria Jose Castro-Bleda, Adrian Palacios-Corella • PLoS ONE (2018)

This paper presents a new method to reduce the computational cost when using Neural Networks as Language Models, during recognition, in some particular scenarios. It is based on a Neural Network that considers input contexts of different le...

DOAJ Open Access (DOAJ) DOI: 10.1371/journal.pone.0200884

Artificial neural networks

Judith E. Dayhoff, James M. DeLeo • Cancer (2001)

Artificial neural networks now are used in many fields. They have become well established as viable, multipurpose, robust computational methodologies with solid theoretic support and with strong potential to be effective in any discipline, ...

OPENALEX Open Access DOI: https://doi.org/10.1002/1097-0142(20010415)91:8+<1615::aid-cncr1175>3.0.co;2-l

3D Octave and 2D Vanilla Mixed Convolutional Neural Network for Hyperspectral Image Classification with Limited Samples

Yuchao Feng, Jianwei Zheng, Mengjie Qin, Cong Bai et al. • Remote Sensing (2021)

Owing to the outstanding feature extraction capability, convolutional neural networks (CNNs) have been widely applied in hyperspectral image (HSI) classification problems and have achieved an impressive performance. However, it is well know...

DOAJ Open Access (DOAJ) DOI: 10.3390/rs13214407

A Revision of Empirical Models of Stirling Engine Performance Using Simple Artificial Neural Networks

Enrique González-Plaza, David García, Jesús-Ignacio Prieto • Inventions (2023)

Stirling engines are currently of interest due to their adaptability to a wide range of energy sources. Since simple tools are needed to guide the sizing of prototypes in preliminary studies, this paper proposes two groups of simple models ...

DOAJ Open Access (DOAJ) DOI: 10.3390/inventions8040088

Formative assessment: a key to deep learning?

Alison Beverley Rushton • Medical Teacher (2005)

A paradigm shift in assessment culture has emphasized the importance of formative assessment. The existing evidence supports the identification of feedback as the central component of formative assessment. Feedback provides information abou...

OPENALEX DOI: https://doi.org/10.1080/01421590500129159

Deep Learning

Dulani Meedeniya • Chapman & Hall/CRC eBooks (2023)

This book focuses on deep learning (DL), which is an important aspect of data science, that includes predictive modeling. DL applications are widely used in domains such as finance, transport, healthcare, automanufacturing, and advertising....

OPENALEX public-domain DOI: https://doi.org/10.1201/9781003390824

Applications of deep learning to MRI images: A survey

Jin Liu, Yi Feng Pan, Min Li, Ziyue Chen et al. • Big Data Mining and Analytics (2018)

Deep learning provides exciting solutions in many fields, such as image analysis, natural language processing, and expert system, and is seen as a key method for various future applications. On account of its non-invasive and good soft tiss...

OPENALEX cc-by DOI: https://doi.org/10.26599/bdma.2018.9020001

Deep learning for digital holography: a review

Tianjiao Zeng, Yanmin Zhu, Edmund Y. M. Lam • Optics Express (2021)

Recent years have witnessed the unprecedented progress of deep learning applications in digital holography (DH). Nevertheless, there remain huge potentials in how deep learning can further improve performance and enable new functionalities ...

OPENALEX Open Access DOI: https://doi.org/10.1364/oe.443367