We propose an extensive study into the effects of differentially private training (dp) on differentially private deep neural networks (dnns), especially on medicalimaging applications, on the data set of the aptos dataset.
We study the effects of differentially private training on the interpretability of these models, and how the application of differentially private training affects the quality of interpretations.
Large pre-trained deep neural networks (dnns) have revolutionized the field of computer vision (cv), but application in industry is often precluded for three reasons : 1) large pre-trained dnns are built on hundreds of millions of parameters, making deployment on many devices impossible, 2) the underlying dataset for pre-training consists of general objects, while industrial cases often consist of very specific objects, such as structures on solar wafers, 3) potentially biased pre-trained dnns raise legal issues for companies.
As a remedy, we study neural networks for cv that we train from scratch.
In this paper, we provide a comprehensive overview on understanding, visualization, and explanation of the internal and overall behavior of deep neural networks (including deep learning architectures).
The aim of this paper is to provide a comprehensive overview on understanding, visualization, and explanation of the internal and overall behavior of deep neural networks (including deep learning architectures).
Deep neural networks (dnns) have been successfully applied to many real-world problems, but a complete understanding of their dynamical and computational principles is still lacking.
Here, by weaving together theories of heavy-tailed random matrices and non-equilibrium statistical physics, we develop a new type of meanfield theory for dnns which predicts that heavy-tailed weights enable the emergence of an extended critical regime without fine-tuning parameters.