GitHub

Blind Nonlinear Unmixing for Intimate Mixtures Using Hapke Model and Convolutional Neural Network

If you use this code and/or our simulated datasets please do not forget cite the following paper: B. Rasti, B. Koirala, and P. Scheunders, "HapkeCNN: Blind Nonlinear Unmixing for Intimate Mixtures ...
eLife

Inferring the time-varying coupling of dynamical systems with temporal convolutional autoencoders

This important work presents a novel approach to infer causal relations in non-stationary time series data. To do so, the authors introduce a novel machine-learning model of Temporal Autoencoders for ...
Scientific Research Publishing

Method of Multi-Mode Sensor Data Fusion with an Adaptive Deep Coupling Convolutional Auto-Encoder ()

To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the ...
Scientific Research Publishing

End-to-End Auto-Encoder System for Deep Residual Shrinkage Network for AWGN Channels ()

1 School of Mathematical Sciences, Guizhou Normal University, Guiyang, China. 2 School of Big Data and Computer Science, Guizhou Normal University, Guiyang, China. With the rapid development of deep ...
Frontiers

Convolutional Neural Networks for Very Low-Dimensional LPV Approximations of Incompressible Navier-Stokes Equations

The control of general nonlinear systems is a challenging task in particular for large-scale models as they occur in the semi-discretization of partial differential equations (PDEs) of, say, fluid ...
Frontiers

Multi-Dimensional Enhanced Seizure Prediction Framework Based on Graph Convolutional Network

In terms of seizure prediction, how to fully mine relational data information among multiple channels of epileptic EEG? This is a scientific research subject worthy of further exploration. Recently, ...
eLife

Interpreting wide-band neural activity using convolutional neural networks

Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data requires ...
acm.org

ImageNet Classification with Deep Convolutional Neural Networks

Five ILSVRC-2010 test images in the first column. Remaining columns show the training images that produce feature vectors in the last hidden layer with the smallest Euclidean distance from the feature ...