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Found 17 papers, 6 papers with code
Deep Unfolding for Iterative Stripe Noise Removal
As the applications of degraded infrared images are limited, it is crucial to effectively preserve original details.
MICAL: Mutual Information-Based CNN-Aided Learned Factor
Since the soft estimates obtained as the combined features from the neural MI estimator and the CNN do not capture the temporal correlation between different EEG blocks, we use them not as estimates of the seizure state, but to compute the function nodes of a factor graph.
A Perspective on Neural Capacity Estimation: Viability and Reliability
These estimators ar referred to as neural mutual information estimation (NMIE)s. NMIEs differ from other approaches as they are data-driven estimators.
CNN-Aided Factor Graphs with Estimated Mutual Information Features for Seizure Detection
We then use a 1D-CNN to extract extra features from the EEG signals and use both features to estimate the probability of a seizure event.~Finally, learned factor graphs are employed to capture the temporal correlation in the signal.
Neural Capacity Estimators: How Reliable Are They?
Recently, several methods have been proposed for estimating the mutual information from sample data using deep neural networks and without the knowing closed form distribution of the data.
Efficient Epileptic Seizure Detection Using CNN-Aided Factor Graphs
We propose a computationally efficient algorithm for seizure detection.
Model-Based Machine Learning for Communications
We present an introduction to model-based machine learning for communication systems.
Learned Factor Graphs for Inference from Stationary Time Sequences
Learned factor graph can be realized using compact neural networks that are trainable using small training sets, or alternatively, be used to improve upon existing deep inference systems.
Data-Driven Symbol Detection via Model-Based Machine Learning
The design of symbol detectors in digital communication systems has traditionally relied on statistical channel models that describe the relation between the transmitted symbols and the observed signal at the receiver.
Data-Driven Factor Graphs for Deep Symbol Detection
In particular, we propose to use machine learning (ML) tools to learn the factor graph, instead of the overall system task, which in turn is used for inference by message passing over the learned graph.
Deep Neural Network Symbol Detection for Millimeter Wave Communications
This paper proposes to use a deep neural network (DNN)-based symbol detector for mmWave systems such that CSI acquisition can be bypassed.
ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection
Our numerical evaluations demonstrate that the performance of ViterbiNet, which is ignorant of the CSI, approaches that of the CSI-based Viterbi algorithm, and is capable of tracking time-varying channels without needing instantaneous CSI or additional training data.
Sliding Bidirectional Recurrent Neural Networks for Sequence Detection in Communication Systems
The design and analysis of communication systems typically rely on the development of mathematical models that describe the underlying communication channel.
Deep Learning for Joint Source-Channel Coding of Text
We consider the problem of joint source and channel coding of structured data such as natural language over a noisy channel.
Neural Network Detection of Data Sequences in Communication Systems
We consider detection based on deep learning, and show it is possible to train detectors that perform well without any knowledge of the underlying channel models.
Detection Algorithms for Communication Systems Using Deep Learning
The design and analysis of communication systems typically rely on the development of mathematical models that describe the underlying communication channel, which dictates the relationship between the transmitted and the received signals.
A Novel Experimental Platform for In-Vessel Multi-Chemical Molecular Communications
This work presents a new multi-chemical experimental platform for molecular communication where the transmitter can release different chemicals.
