Results 121 to 130 of about 8,972,433 (325)

Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

Nature Communications, 2019
Machine learning models can accurately predict atomistic chemical properties but do not provide access to the molecular electronic structure. Here the authors use a deep learning approach to predict the quantum mechanical wavefunction at high efficiency ...
K. T. Schütt, M. Gastegger, A. Tkatchenko, K.-R. Müller, R. J. Maurer   +4 more
doaj   +1 more source

Machine Learning for Clinical Predictive Analytics [PDF]

arXiv, 2019
In this chapter, we provide a brief overview of applying machine learning techniques for clinical prediction tasks. We begin with a quick introduction to the concepts of machine learning and outline some of the most common machine learning algorithms. Next, we demonstrate how to apply the algorithms with appropriate toolkits to conduct machine learning
arxiv  

Algorithms & Fiduciaries: Existing and Proposed Regulatory Approaches to Artificially Intelligent Financial Planners [PDF]

, 2017
Artificial intelligence is no longer solely in the realm of science fiction. Today, basic forms of machine learning algorithms are commonly used by a variety of companies.
Lightbourne, John
core   +1 more source

Introduction to Machine Learning: Class Notes 67577 [PDF]

arXiv, 2009
Introduction to Machine learning covering Statistical Inference (Bayes, EM, ML/MaxEnt duality), algebraic and spectral methods (PCA, LDA, CCA, Clustering), and PAC learning (the Formal model, VC dimension, Double Sampling theorem).
arxiv  

Accelerating crystal structure search through active learning with neural networks for rapid relaxations

npj Computational Materials
Global optimization of crystal compositions is a significant yet computationally intensive method to identify stable structures within chemical space.
Stefaan S. P. Hessmann, Kristof T. Schütt, Niklas W. A. Gebauer, Michael Gastegger, Tamio Oguchi, Tomoki Yamashita   +5 more
doaj   +1 more source

Enhanced dose prediction for head and neck cancer artificial intelligence‐driven radiotherapy based on transfer learning with limited training data

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract Purpose Training deep learning dose prediction models for the latest cutting‐edge radiotherapy techniques, such as AI‐based nodal radiotherapy (AINRT) and Daily Adaptive AI‐based nodal radiotherapy (DA‐AINRT), is challenging due to limited data.
Hui‐Ju Wang, Austen Maniscalco, David Sher, Mu‐Han Lin, Steve Jiang, Dan Nguyen   +5 more
wiley   +1 more source

Review on the Advancements in Wind Turbine Blade Inspection: Integrating Drone and Deep Learning Technologies for Enhanced Defect Detection

IEEE Access
The increasing demand for wind power requires more frequent inspections to identify defects in the Wind Turbine Blades (WTBs). These defects, if not detected, can compromise the structural integrity and safety of wind turbines.
Majid Memari, Praveen Shakya, Mohammad Shekaramiz, Abdennour C. Seibi, Mohammad A. S. Masoum   +4 more
doaj   +1 more source

Industrial Machine Learning Is Not Academic Machine Learning

, 2018
State Treasury Office of Ceara – Fortaleza, CE ...
openaire   +2 more sources

The effect of multi‐leaf collimator leaf width on VMAT treatment plan quality

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract Background The advent of volumetric modulated arc therapy (VMAT) in radiotherapy has made it one of the most commonly used techniques in clinical practice. VMAT is the delivery of intensity modulated radiation therapy (IMRT) while the gantry is in motion, and existing literature has shown it has decreased treatment delivery times and the ...
Gregory Sadharanu Peiris, Brendan Whelan, Nicholas Hardcastle, Suzie Lynn Sheehy   +3 more
wiley   +1 more source

Machine Learning Proceedings 1988 [PDF]

, 1988

openalex   +2 more sources