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1) âTargeted Fine-Tuning of DNN-Based Receivers via Influence Functionsâ by Tuononen, Penttinen, HautamĂ€ki (StatML) arxiv.org/abs/2509.15950
Influence functions pinpoint the training samples behind bit decisions, enabling targeted fine-tuning that improves BER (single-target > random).
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 training samples drive b...
