Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms
M AlQuraishi, PK Sorger - Nature methods, 2021 - nature.com
Deep learning using neural networks relies on a class of machine-learnable models
constructed using 'differentiable programs'. These programs can combine mathematical …
constructed using 'differentiable programs'. These programs can combine mathematical …
Status and prospects for drought forecasting: Opportunities in artificial intelligence and hybrid physical–statistical forecasting
A AghaKouchak, B Pan… - … of the Royal …, 2022 - royalsocietypublishing.org
Despite major improvements in weather and climate modelling and substantial increases in
remotely sensed observations, drought prediction remains a major challenge. After a review …
remotely sensed observations, drought prediction remains a major challenge. After a review …
Understanding batch normalization
Batch normalization (BN) is a technique to normalize activations in intermediate layers of
deep neural networks. Its tendency to improve accuracy and speed up training have …
deep neural networks. Its tendency to improve accuracy and speed up training have …
Nonlinear black-box modeling in system identification: a unified overview
A nonlinear black-box structure for a dynamical system is a model structure that is prepared
to describe virtually any nonlinear dynamics. There has been considerable recent interest in …
to describe virtually any nonlinear dynamics. There has been considerable recent interest in …
[PDF][PDF] Neural network fundamentals with graphs, algorithms, and applications
P Liang, NK Bose - Mac Graw-Hill, 1996 - ggnindia.dronacharya.info
NEURAL NETWORK FUNDAMENTALS WITH GRAPHS, ALGORITHMS, AND APPLICATIONS
Page 1 NEURAL NETWORK FUNDAMENTALS WITH GRAPHS, ALGORITHMS, AND …
Page 1 NEURAL NETWORK FUNDAMENTALS WITH GRAPHS, ALGORITHMS, AND …
[图书][B] Least squares data fitting with applications
A lucid explanation of the intricacies of both simple and complex least squares methods. As
one of the classical statistical regression techniques, and often the first to be taught to new …
one of the classical statistical regression techniques, and often the first to be taught to new …
Nonlinear black-box modeling in system identification: a unified overview
A nonlinear black-box structure for a dynamical system is a model structure that is prepared
to describe virtually any nonlinear dynamics. There has been considerable recent interest in …
to describe virtually any nonlinear dynamics. There has been considerable recent interest in …
[图书][B] Artificial neural networks for modelling and control of non-linear systems
Artificial neural networks possess several properties that make them particularly attractive for
applications to modelling and control of complex non-linear systems. Among these …
applications to modelling and control of complex non-linear systems. Among these …
JETNET 3.0—A versatile artificial neural network package
An F77 package for feed-forward artificial neural network data processing, JETNET 3.0, is
presented. It represents a substantial extension and generalization of an earlier release …
presented. It represents a substantial extension and generalization of an earlier release …