Search Results for author:
Found 3 papers, 0 papers with code
Exact Count of Boundary Pieces of ReLU Classifiers: Towards the Proper Complexity Measure for Classification
Classic learning theory suggests that proper regularization is the key to good generalization and robustness.
Rate of Convergence of Polynomial Networks to Gaussian Processes
For networks with a polynomial activation, we demonstrate that the rate of this convergence in 2-Wasserstein metric is $O(n^{-\frac{1}{2}})$, where $n$ is the number of hidden neurons.
Gradient Explosion and Representation Shrinkage in Infinite Networks
We study deep fully-connected neural networks using the mean field formalism, and carry out a non-perturbative analysis of signal propagation.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
