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Software-defined Optoacoustic Tomography
In this work we present the first application of software-defined optoelectronics (SDO) for bidimensional optoacoustic tomography (OAT).
Noisy Learning for Neural ODEs Acts as a Robustness Locus Widening
We investigate the problems and challenges of evaluating the robustness of Differential Equation-based (DE) networks against synthetic distribution shifts.
Realization Theory Of Recurrent Neural ODEs Using Polynomial System Embeddings
In this paper we show that neural ODE analogs of recurrent (ODE-RNN) and Long Short-Term Memory (ODE-LSTM) networks can be algorithmically embeddeded into the class of polynomial systems.
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