Search Results for author:
Found 4 papers, 0 papers with code
Self-Regularity of Non-Negative Output Weights for Overparameterized Two-Layer Neural Networks
Using a simple covering number argument, we establish that under quite mild distributional assumptions on the input/label pairs; any such network achieving a small training error on polynomially many data necessarily has a well-controlled outer norm.
Neural Networks and Polynomial Regression. Demystifying the Overparametrization Phenomena
Thus a message implied by our results is that parametrizing wide neural networks by the number of hidden nodes is misleading, and a more fitting measure of parametrization complexity is the number of regression coefficients associated with tensorized data.
Stationary Points of Shallow Neural Networks with Quadratic Activation Function
Next, we show that initializing below this barrier is in fact easily achieved when the weights are randomly generated under relatively weak assumptions.
Inference in High-Dimensional Linear Regression via Lattice Basis Reduction and Integer Relation Detection
Using a novel combination of the PSLQ integer relation detection, and LLL lattice basis reduction algorithms, we propose a polynomial-time algorithm which provably recovers a $\beta^*\in\mathbb{R}^p$ enjoying the mixed-support assumption, from its linear measurements $Y=X\beta^*\in\mathbb{R}^n$ for a large class of distributions for the random entries of $X$, even with one measurement $(n=1)$.
