Results 21 to 30 of about 65,893 (254)
MathematicsIn this article, we study the multivariate quantitative smooth approximation under differentiation of functions. The approximators here are multivariate neural network operators activated by the symmetrized and perturbed hyperbolic tangent activation ...
George A. Anastassiou
doaj +1 more source
A program for the Bayesian Neural Network in the ROOT framework
, 2011 We present a Bayesian Neural Network algorithm implemented in the TMVA package, within the ROOT framework. Comparing to the conventional utilization of Neural Network as discriminator, this new implementation has more advantages as a non-parametric ...
Brun +11 more
core +1 more source
Neural Network Gradient Hamiltonian Monte Carlo
, 2018 Hamiltonian Monte Carlo is a widely used algorithm for sampling from posterior distributions of complex Bayesian models. It can efficiently explore high-dimensional parameter spaces guided by simulated Hamiltonian flows.
Baldi, Pierre +3 more
core +2 more sources
Soft Computation Vector Autoregressive Neural Network (VAR-NN) GUI-Based
E3S Web of Conferences, 2018 Vector autoregressive model proposed for multivariate time series data. Neural Network, including Feed Forward Neural Network (FFNN), is the powerful tool for the nonlinear model.
Yasin Hasbi +3 more
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Privacy-Preserving Adversarial Networks
, 2019 We propose a data-driven framework for optimizing privacy-preserving data release mechanisms to attain the information-theoretically optimal tradeoff between minimizing distortion of useful data and concealing specific sensitive information. Our approach
Ishwar, Prakash +2 more
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MathematicsThis paper deals with a family of normalized multivariate neural network (MNN) operators of complex-valued continuous functions for a multivariate context on a box of RN¯, N¯∈N.
Seda Karateke
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A Graph Algorithmic Approach to Separate Direct from Indirect Neural Interactions.
PLoS ONE, 2015 Network graphs have become a popular tool to represent complex systems composed of many interacting subunits; especially in neuroscience, network graphs are increasingly used to represent and analyze functional interactions between multiple neural ...
Patricia Wollstadt +2 more
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Multivariate hyperbolic tangent neural network approximation
Computers & Mathematics with Applications, 2011 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire +3 more sources
Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
Advanced Functional Materials, EarlyView.A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore +7 more
wiley +1 more source
Relative entropy minimizing noisy non-linear neural network to approximate stochastic processes
, 2014 A method is provided for designing and training noise-driven recurrent neural networks as models of stochastic processes. The method unifies and generalizes two known separate modeling approaches, Echo State Networks (ESN) and Linear Inverse Modeling ...
Galtier, Mathieu N. +3 more
core +2 more sources