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Found 8 papers, 2 papers with code
Engineered Ordinary Differential Equations as Classification Algorithm (EODECA): thorough characterization and testing
EODECA (Engineered Ordinary Differential Equations as Classification Algorithm) is a novel approach at the intersection of machine learning and dynamical systems theory, presenting a unique framework for classification tasks [1].
Complex Recurrent Spectral Network
This paper presents a novel approach to advancing artificial intelligence (AI) through the development of the Complex Recurrent Spectral Network ($\mathbb{C}$-RSN), an innovative variant of the Recurrent Spectral Network (RSN) model.
A Bridge between Dynamical Systems and Machine Learning: Engineered Ordinary Differential Equations as Classification Algorithm (EODECA)
Recognizing the deep parallels between dense neural networks and dynamical systems, particularly in the light of non-linearities and successive transformations, this manuscript introduces the Engineered Ordinary Differential Equations as Classification Algorithms (EODECAs).
How a student becomes a teacher: learning and forgetting through Spectral methods
In theoretical ML, the teacher-student paradigm is often employed as an effective metaphor for real-life tuition.
Recurrent Spectral Network (RSN): shaping the basin of attraction of a discrete map to reach automated classification
A novel strategy to automated classification is introduced which exploits a fully trained dynamical system to steer items belonging to different categories toward distinct asymptotic attractors.
On the training of sparse and dense deep neural networks: less parameters, same performance
Deep neural networks can be trained in reciprocal space, by acting on the eigenvalues and eigenvectors of suitable transfer operators in direct space.
Mobility-based prediction of SARS-CoV-2 spreading
The rapid spreading of SARS-CoV-2 and its dramatic consequences, are forcing policymakers to take strict measures in order to keep the population safe.
Disordered Systems and Neural Networks
Machine learning in spectral domain
Interestingly, spectral learning limited to the eigenvalues returns a distribution of the predicted weights which is close to that obtained when training the neural network in direct space, with no restrictions on the parameters to be tuned.
