Artificial neural networks (NNs) have been widely used in creation of various machine learning (ML) models. Training an NN for a given dataset is essential... GR Liu - 《International Journal of Computational Methods》 被引量: 0发表: 2022年 Solution Existence Theory for Artificial Neural Networ...
Advancement in machine learning (ML) field is rising computational demands during deep learning (DL) applications development process. Emphasis on statistical, theoretical, and computational aspects of learning and graphical models, networked with sparse analysis, tensor, and topological methods, numerical ...
(1995). A decision theoretic generalization on on-line learning and an application to bosting. Computational Learning Theory. 2nd European Conference, EuroCOLT'95, pp. 23-27. http://www.research.att.com/orgs/ssr/people/yoav Fukunaga, K. (1990). Introduction to Statistical Pattern Recognition...
The investigation compares the conventional, advanced machine, deep, and hybrid learning models to introduce an optimum computational model to assess the ground vibrations during blasting in mining projects. The long short-term memory (LSTM), artificial neural network (ANN), least square support vector...
MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows. J. Chem. Theory Comput. 2024, 20, 1193–1213. DOI: 10.1021/acs.jctc.3c01203. Blog post › | Tutorial › Book “Quantum Chemistry in the Age of Machine Learning” Quantum Chemistry in ...
On the histogram as a density estimator: L2 theory. Z. Wahrscheinlichkeitstheorie Verwandte Gebiete 57, 453–476 (1981). Article MathSciNet Google Scholar Brader, J. M., Senn, W. & Fusi, S. Learning real-world stimuli in a neural network with spike-driven synaptic dynamics. Neural ...
The original paper on the dual space search as scientific thinking theory. Complexity Management in a Discovery Task - CogSci'92, 1992. [All Versions]. Advanced experiments on dual space search. A dual-space model of iteratively deepening exploratory learning - International Journal of Human-...
In this paper, we propose a general framework for sparse and low-rank tensor estimation from cubic sketchings. A two-stage non-convex implementation is dev... B Hao,A Zhang,G Cheng - 《IEEE Transactions on Information Theory》 被引量: 7发表: 2018年 AN OPTIMAL STATISTICAL AND COMPUTATIONAL ...
Machine learning (ML) has become critical for post-acquisition data analysis in (scanning) transmission electron microscopy, (S)TEM, imaging and spectroscopy. An emerging trend is the transition to real-time analysis and closed-loop microscope operation.
We also show that this algorithm can be used to build a remarkably simple machine-learning model capable of outperforming deep-learning models in detecting Parkinson’s disease from a single digital handwriting test.Similar content being viewed by others Reconstructing computational system dynamics from ...