52 citations · 116 across the 22 of their papers we have counts for
3 papers · 1 filter
Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions
Elisabetta Cornacchia, Dan Mikulincer, Elchanan Mossel
The problem of learning single index and multi index models has gained significant interest as a fundamental task in high-dimensional statistics. Many recent works have analysed gr…
Size and depth of monotone neural networks: interpolation and approximation
Dan Mikulincer, Daniel Reichman
We study monotone neural networks with threshold gates where all the weights (other than the biases) are non-negative. We focus on the expressive power and efficiency of representa…
Network size and weights size for memorization with two-layers neural networks
Sébastien Bubeck, Ronen Eldan, Yin Tat Lee +1
In 1988, Eric B. Baum showed that two-layers neural networks with threshold activation function can perfectly memorize the binary labels of points in general position in $\math…