3 accepted papers
Matrix sensing has many real-world applications in science and engineering, such as system control, distance embedding, and computer vision. The goal of matrix sensing is to recover a matrix $A_\star \in \mathbb{R}^{n \times n}$, based on a sequence of measurements $(u_i,b_i) \in \mathbb{R}^{n} \tim…
Adversarial training is a widely used strategy for making neural networks resistant to adversarial perturbations. For a neural network of width $m$, $n$ input training data in $d$ dimension, it takes $\Omega(mnd)$ time cost per training iteration for the forward and backward computation. In this pap…
Online matrix vector multiplication is a fundamental step and bottleneck in many machine learning algorithms. It is defined as follows: given a matrix at the pre-processing phase, at each iteration one receives a query vector and needs to form the matrix-vector product (approximately) before observi…