Robustness of Classifiers to Universal Perturbations: A Geometric Perspective

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Polynomial neural networks (PNNs) have been recently shown to be particularly effective at image generation and face recognition, where high-frequency information is critical. Previous studies have revealed that neural networks demonstrate a spectral bias ...

Animal behavior emerges from an interaction between neural network dynamics, musculoskeletal properties and the physical environment. Accessing and understanding the interplay between these elements requires the development of integrative and morphological ...

Neural networks are ubiquitous in applied machine learning for education. Their pervasive success in predictive performance comes alongside a severe weakness, the lack of explainability of their decisions, especially relevant in human-centric fields. We im ...

Most available studies on distributed GAN architectures focus on implementations with a fusion center. In this work, we propose a fully decentralized scheme by employing a diffusion strategy to train a network of GANs. We interpret the training procedure a ...

Robustness of Classifiers to Universal Perturbations: A Geometric Perspective

Chattez avec Graph Search

The spectral bias of polynomial neural networks

NeuroMechFly, a neuromechanical model of adult Drosophila melanogaster

Evaluating the Explainers: Black-Box Explainable Machine Learning for Student Success Prediction in MOOCs

Decentralized Gan Training Through Diffusion Learning