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NN2Poly: A Polynomial Representation for Deep Feed-Forward Artificial Neural Networks

• Authors : Pablo Morala; Jenny Alexandra Cifuentes; Rosa E. Lillo ...

Subjects: FOS: Computer and information sciences; Computer Science - Machine Learning; Multiset partitions

• Source: e-Archivo. Repositorio Institucional de la Universidad Carlos III de MadridUniversidad Carlos III de Madrid (UC3M)

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FAST: Boosting Uncertainty-based Test Prioritization Methods for Neural Networks via Feature Selection

• Authors : Jialuo Chen; Jingyi Wang; Xiyue Zhang

Subjects: Software Engineering (cs.SE); FOS: Computer and information sciences; Deep Neural Networks

• Source: Chen, J, Wang, J, Zhang, X, Sun, Y, Kwiatkowska, M, Chen, J & Cheng, P 2024, FAST: Boosting Uncertainty-based Test Prioritization Methods for Neural

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Compressed Latent Replays for Lightweight Continual Learning on Spiking Neural Networks

• Authors : Dequino, Alberto; Carpegna, Alessio; Nadalini, Davide

Subjects: FOS: Computer and information sciences; Computer Science - Machine Learning; Artificial Intelligence (cs.AI)

• Source: 2024 IEEE Computer Society Annual Symposium on VLSI (ISVLSI). :240-245

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Adversarial attacks on neural networks through canonical Riemannian foliations

• Authors : Eliot Tron; Nicolas Couëllan; Stéphane Puechmorel ...

Subjects: Mathematics - Differential Geometry; FOS: Computer and information sciences; Computer Science - Machine Learning

• Source: Machine LearningMachine Learning, In press

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Explainability through uncertainty: Trustworthy decision-making with neural networks

• Authors : Arthur Thuy; Dries F. Benoit

Subjects: FOS: Computer and information sciences; Computer Science - Machine Learning; Neural Networks

• Source: EUROPEAN JOURNAL OF OPERATIONAL RESEARCH

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PHNNs: Lightweight Neural Networks via Parameterized Hypercomplex Convolutions

• Authors : Eleonora Grassucci; Aston Zhang; Danilo Comminiello

Subjects: FOS: Computer and information sciences; Computer Science - Machine Learning; Computer Science - Artificial Intelligence

• Source: IEEE Transactions on Neural Networks and Learning Systems. 35:8293-8305

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Emergent weight morphologies in deep neural networks

• Authors : de Jong, Pascal; Meigel, Felix; Rulands, Steffen

Subjects: FOS: Computer and information sciences; Computer Science - Machine Learning; FOS: Physical sciences

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Statistical Mechanics and Artificial Neural Networks: Principles, Models, and Applications

• Authors : Lucas Böttcher; Gregory Wheeler

Subjects: FOS: Computer and information sciences; Computer Science - Machine Learning; Artificial Intelligence (cs.AI)

• Source: Order, Disorder and Criticality ISBN: 9789819800810

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Perceptron Theory Can Predict the Accuracy of Neural Networks

• Authors : Kleyko, Denis; Rosato, Antonello; Frady, Edward Paxon

Subjects: FOS: Computer and information sciences; Computer Science - Machine Learning; Artificial Intelligence (cs.AI)

• Source: IEEE Transactions on Neural Networks and Learning Systems

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Solution Space and Storage Capacity of Fully Connected Two-Layer Neural Networks with Generic Activation Functions

• Authors : Sota Nishiyama; Masayuki Ohzeki

Subjects: FOS: Computer and information sciences; Computer Science - Machine Learning; Statistics - Machine Learning

• Source: Journal of the Physical Society of Japan. 94

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