In many applied research fields—including Geophysics, Medicine, Engineering, Economics, and Finance— fundamental challenges involve extracting hidden information and meaningful features from complex signals, such as quasi-periodicities, time-varying frequency patterns, and underlying components like trends.
Classical signal processing techniques based on Fourier and Wavelet transforms,...
In signal processing, the time-frequency analysis of nonlinear and non-stationary processes, as well as the determination of the unknown number of active sub-signals in a blind-source composite signal, are generally challenging inverse problem tasks. If we consider data sampled on a sphere, things get even more complicated. This is the reason why just a few techniques have been developed so...
Accuracy–cost trade-offs and data and parameter efficiency are two fundamental aspects of machine learning for scientific computing. In the first part of this talk, we address test-time control of model performance. We introduce the Recurrent-Depth Simulator (RecurrSim), an architecture-agnostic framework that enables explicit control over accuracy–cost trade-offs in neural simulators without...
Recent empirical and theoretical results suggest that deep networks possess an implicit low-rank bias: their weight matrices naturally evolve toward approximately low-rank structure, and a structured pruning of small singular values can often reduce model size with little or no loss in accuracy. While this phenomenon is already understood in simplified settings, a complete theory accounting...
