7 citations · 7 across the 4 of their papers we have counts for
6 papers
Robust Transfer Learning for Paper ECG Recognition
Yinghao Xie, Zhenbang Dai, Haojun Wang +6
Paper ECG recognition is challenging because real-world ECG images vary in layout, physical artifacts, and label availability. We introduce RobECG-CL, a rank-aware contrastive lear…
On the role of the tokenizer in ECG transformer models
Jiawei Li, Fabio Bonassi, Johan Sundström +2
Tokenization determines both the physiological content presented to an ECG Transformer and the sequence over which attention operates. We compare eight tokenization strategies acro…
How Do Electrocardiogram Models Scale?
Jiawei Li, Fabio Bonassi, Ming Jin +4
While scaling laws have established a fundamental framework for foundation models in natural language processing, their applicability to electrocardiogram (ECG) models remains poor…
Accounts of using the Tustin-Net architecture on a rotary inverted pendulum
Stijn van Esch, Fabio Bonassi, Thomas B. Schön
In this report we investigate the use of the Tustin neural network architecture (Tustin-Net) for the identification of a physical rotary inverse pendulum. This physics-based archit…
On the equivalence of direct and indirect data-driven predictive control approaches
Per Mattsson, Fabio Bonassi, Valentina Breschi +1
Recently, several direct Data-Driven Predictive Control (DDPC) methods have been proposed, advocating the possibility of designing predictive controllers from historical input-outp…
Structured state-space models are deep Wiener models
Fabio Bonassi, Carl Andersson, Per Mattsson +1
The goal of this paper is to provide a system identification-friendly introduction to the Structured State-space Models (SSMs). These models have become recently popular in the mac…