From the 1 of 5 linked papers with an AI index.
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NMINE: Normalized Mutual Information Neural Estimation
Petra Eerikinharju, Marko Tuononen, Ville Hautamäki
The paper introduces a fully neural estimator for normalized mutual information of continuous, multidimensional variables, combining a MINE-based mutual information estimator with…
Targeted Fine-Tuning of DNN-Based Receivers via Influence Functions
Marko Tuononen, Heikki Penttinen, Ville Hautamäki
We present the first use of influence functions for deep learning-based wireless receivers. Applied to DeepRx, a fully convolutional receiver, influence analysis reveals which trai…
Out-of-Distribution Detection via Channelwise Feature Aggregation in Neural Network-Based Receivers
Marko Tuononen, Heikki Penttinen, Duy Vu +3
Neural network-based radio receivers are expected to play a key role in future wireless systems, making reliable Out-Of-Distribution (OOD) detection essential. We propose a post-ho…
Interpreting Deep Neural Network-Based Receiver Under Varying Signal-To-Noise Ratios
Marko Tuononen, Dani Korpi, Ville Hautamäki
We propose a novel method for interpreting neural networks, focusing on convolutional neural network-based receiver model. The method identifies which unit or units of the model co…