1 citations · 1 across the 2 of their papers we have counts for
5 papers
Zero-shot World Models via Search in Memory
Federico Malato, Ville Hautamäki
World Models have vastly permeated the field of Reinforcement Learning. Their ability to model the transition dynamics of an environment have greatly improved sample efficiency in…
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…
Generalizable speech deepfake detection via meta-learned LoRA
Janne Laakkonen, Ivan Kukanov, Ville Hautamäki
Reliable detection of speech deepfakes (spoofs) must remain effective when the distribution of spoofing attacks shifts. We frame the task as domain generalization and show that ins…
Improving Numerical Stability of Normalized Mutual Information Estimator on High Dimensions
Marko Tuononen, Ville Hautamäki
Mutual information provides a powerful, general-purpose metric for quantifying the amount of shared information between variables. Estimating normalized mutual information using a…
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…