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Found 6 papers, 0 papers with code
Size and depth of monotone neural networks: interpolation and approximation
Monotone functions and data sets arise in a variety of applications.
Non-asymptotic approximations of neural networks by Gaussian processes
We study the extent to which wide neural networks may be approximated by Gaussian processes when initialized with random weights.
Network size and size of the weights in memorization with two-layers neural networks
In contrast we propose a new training procedure for ReLU networks, based on {\em complex} (as opposed to {\em real}) recombination of the neurons, for which we show approximate memorization with both $O\left(\frac{n}{d} \cdot \frac{\log(1/\epsilon)}{\epsilon}\right)$ neurons, as well as nearly-optimal size of the weights.
Community detection and percolation of information in a geometric setting
We make the first steps towards generalizing the theory of stochastic block models, in the sparse regime, towards a model where the discrete community structure is replaced by an underlying geometry.
Network size and weights size for memorization with two-layers neural networks
In contrast we propose a new training procedure for ReLU networks, based on complex (as opposed to real) recombination of the neurons, for which we show approximate memorization with both $O\left(\frac{n}{d} \cdot \frac{\log(1/\epsilon)}{\epsilon}\right)$ neurons, as well as nearly-optimal size of the weights.
How to trap a gradient flow
This viewpoint was explored in 1993 by Vavasis, who proposed an algorithm which, for any fixed finite dimension $d$, improves upon the $O(1/\varepsilon^2)$ oracle complexity of gradient descent.
