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Found 5 papers, 0 papers with code
Sharp Analysis of Power Iteration for Tensor PCA
We investigate the power iteration algorithm for the tensor PCA model introduced in Richard and Montanari (2014).
Learning time-scales in two-layers neural networks
In this paper, we study the gradient flow dynamics of a wide two-layer neural network in high-dimension, when data are distributed according to a single-index model (i. e., the target function depends on a one-dimensional projection of the covariates).
Lower Bounds for the Convergence of Tensor Power Iteration on Random Overcomplete Models
In this paper, we analyze the dynamics of tensor power iteration from random initialization in the overcomplete regime.
Overparametrized linear dimensionality reductions: From projection pursuit to two-layer neural networks
Denoting by $\mathscr{F}_{m, \alpha}$ the set of probability distributions in $\mathbb{R}^m$ that arise as low-dimensional projections in this limit, we establish new inner and outer bounds on $\mathscr{F}_{m, \alpha}$.
Tractability from overparametrization: The example of the negative perceptron
In the negative perceptron problem we are given $n$ data points $({\boldsymbol x}_i, y_i)$, where ${\boldsymbol x}_i$ is a $d$-dimensional vector and $y_i\in\{+1,-1\}$ is a binary label.
