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Found 5 papers, 1 papers with code
A path-norm toolkit for modern networks: consequences, promises and challenges
The versatility of the toolkit and its ease of implementation allow us to challenge the concrete promises of path-norm-based generalization bounds, by numerically evaluating the sharpest known bounds for ResNets on ImageNet.
Sparsity in neural networks can improve their privacy
This article measures how sparsity can make neural networks more robust to membership inference attacks.
Sparsity in neural networks can increase their privacy
This article measures how sparsity can make neural networks more robust to membership inference attacks.
Approximation speed of quantized vs. unquantized ReLU neural networks and beyond
This is achieved using a new lower-bound on the Lipschitz constant of the map that associates the parameters of ReLU networks to their realization, and an upper-bound generalizing classical results.
Compressive Learning for Semi-Parametric Models
In the compressive learning theory, instead of solving a statistical learning problem from the input data, a so-called sketch is computed from the data prior to learning.
