Complex recurrent spectral network
This paper presents a novel approach to advancing artificial intelligence (AI) through the
development of the Complex Recurrent Spectral Network (ℂ-RSN), an innovative variant of …
development of the Complex Recurrent Spectral Network (ℂ-RSN), an innovative variant of …
Parallel learning by multitasking neural networks
Parallel learning, namely the simultaneous learning of multiple patterns, constitutes a
modern challenge for neural networks. While this cannot be accomplished by standard …
modern challenge for neural networks. While this cannot be accomplished by standard …
Where do hard problems really exist?
R Marino - arxiv preprint arxiv:2309.16253, 2023 - arxiv.org
This chapter delves into the realm of computational complexity, exploring the world of
challenging combinatorial problems and their ties with statistical physics. Our exploration …
challenging combinatorial problems and their ties with statistical physics. Our exploration …
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 …
approach at the intersection of machine learning and dynamical systems theory, presenting …
The Effect on Model Performance of Increasing the Batch Size during Training
İ Akgül - avesis.ebyu.edu.tr
Many hyperparameters are used for classification in convolutional neural networks (CNNs).
Optimum tuning of hyperparameters plays an important role in the classification success of …
Optimum tuning of hyperparameters plays an important role in the classification success of …