22 citations · 22 across the 3 of their papers we have counts for
9 papers
On Small-Depth Tree Augmentations
Ojas Parekh, R. Ravi, Michael Zlatin
We study the Weighted Tree Augmentation Problem for general link costs. We show that the integrality gap of the ODD-LP relaxation for the (weighted) Tree Augmentation Problem for a…
Constant-Depth and Subcubic-Size Threshold Circuits for Matrix Multiplication
Ojas Parekh, Cynthia A. Phillips, Conrad D. James +1
Boolean circuits of McCulloch-Pitts threshold gates are a classic model of neural computation studied heavily in the late 20th century as a model of general computation. Recent adv…
Solving a steady-state PDE using spiking networks and neuromorphic hardware
J. Darby Smith, William Severa, Aaron J. Hill +5
The widely parallel, spiking neural networks of neuromorphic processors can enable computationally powerful formulations. While recent interest has focused on primarily machine lea…
Probing a Set of Trajectories to Maximize Captured Information
Sándor P. Fekete, Alexander Hill, Dominik Krupke +4
We study a trajectory analysis problem we call the Trajectory Capture Problem (TCP), in which, for a given input set of trajectories in the plane, and an integer $k\geq…
Almost optimal classical approximation algorithms for a quantum generalization of Max-Cut
Sevag Gharibian, Ojas Parekh
Approximation algorithms for constraint satisfaction problems (CSPs) are a central direction of study in theoretical computer science. In this work, we study classical product stat…
Spiking Neural Algorithms for Markov Process Random Walk
William Severa, Rich Lehoucq, Ojas Parekh +1
The random walk is a fundamental stochastic process that underlies many numerical tasks in scientific computing applications. We consider here two neural algorithms that can be use…