no code implementations • 14 Oct 2017 • Hassan Ashtiani, Shai Ben-David, Nick Harvey, Christopher Liaw, Abbas Mehrabian, Yaniv Plan
We prove that $\tilde{\Theta}(k d^2 / \varepsilon^2)$ samples are necessary and sufficient for learning a mixture of $k$ Gaussians in $\mathbb{R}^d$, up to error $\varepsilon$ in total variation distance.
no code implementations • 8 Mar 2017 • Peter L. Bartlett, Nick Harvey, Chris Liaw, Abbas Mehrabian
We prove new upper and lower bounds on the VC-dimension of deep neural networks with the ReLU activation function.
