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- Istituto Nazionale di Fisica Nucleare, Sezione di TorinoIT345 papers
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18 papers · 2 filters
Toward cross-subject and cross-session generalization in EEG-based emotion recognition: Systematic review, taxonomy, and methods
Andrea Apicella, Pasquale Arpaia, Giovanni D'Errico +4
A systematic review on machine-learning strategies for improving generalizability (cross-subjects and cross-sessions) electroencephalography (EEG) based in emotion classification w…
Informed Priors for Knowledge Integration in Trajectory Prediction
Christian Schlauch, Nadja Klein, Christian Wirth
Informed machine learning methods allow the integration of prior knowledge into learning systems. This can increase accuracy and robustness or reduce data needs. However, existing…
AccelAT: A Framework for Accelerating the Adversarial Training of Deep Neural Networks through Accuracy Gradient
Farzad Nikfam, Alberto Marchisio, Maurizio Martina +1
Adversarial training is exploited to develop a robust Deep Neural Network (DNN) model against the malicious altered data. These attacks may have catastrophic effects on DNN models…
RoHNAS: A Neural Architecture Search Framework with Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks
Alberto Marchisio, Vojtech Mrazek, Andrea Massa +3
Neural Architecture Search (NAS) algorithms aim at finding efficient Deep Neural Network (DNN) architectures for a given application under given system constraints. DNNs are comput…
Human Activity Recognition on Microcontrollers with Quantized and Adaptive Deep Neural Networks
Francesco Daghero, Alessio Burrello, Chen Xie +6
Human Activity Recognition (HAR) based on inertial data is an increasingly diffused task on embedded devices, from smartphones to ultra low-power sensors. Due to the high computati…
Non-Myopic Multifidelity Bayesian Optimization
Francesco Di Fiore, Laura Mainini
Bayesian optimization is a popular framework for the optimization of black box functions. Multifidelity methods allows to accelerate Bayesian optimization by exploiting low-fidelit…