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pro vyhledávání: '"Suhail, Pirzada"'
Autor:
Suhail, Pirzada, Sethi, Amit
Machine Learning models are often trained on proprietary and private data that cannot be shared, though the trained models themselves are distributed openly assuming that sharing model weights is privacy preserving, as training data is not expected t
Externí odkaz:
http://arxiv.org/abs/2410.16884
Autor:
Suhail, Pirzada, Sethi, Amit
Neural networks have emerged as powerful tools across various applications, yet their decision-making process often remains opaque, leading to them being perceived as "black boxes." This opacity raises concerns about their interpretability and reliab
Externí odkaz:
http://arxiv.org/abs/2407.18002
While the deployment of neural networks, yielding impressive results, becomes more prevalent in various applications, their interpretability and understanding remain a critical challenge. Network inversion, a technique that aims to reconstruct the in
Externí odkaz:
http://arxiv.org/abs/2402.11995