IEEE

Deriving Good LDPC Convolutional Codes from LDPC Block Codes

Abstract: Low-density parity-check (LDPC) convolutional codes are capable of achieving excellent performance with low encoding and decoding complexity. In this paper, we discuss several ...
GitHub

Code for "RDGCN: Reinforced Dependency Graph Convolutional Network for Aspect-based Sentiment Analysis"

1. Download and unzip GloVe vectors (glove.840B.300d.zip) from https://nlp.stanford.edu/projects/glove/ and put it into ./glove/ 2. Download the BERT encoder (bert ...
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 ...
Nature

SEGCN: a subgraph encoding based graph convolutional network model for social bot detection

With the rapid development of Internet technology, cybersecurity incidents in cyberspace are frequent and have a great impact. Countries around the world regard network security situation awareness as ...
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 ...
IEEE

Braided Convolutional Codes: A New Class of Turbo-Like Codes

Abstract: We present a new class of iteratively decodable turbo-like codes, called braided convolutional codes. Constructions and encoding procedures for tightly and sparsely braided convolutional ...
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 ...
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 ...
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 ...