1 citations · 1 across the 12 of their papers we have counts for
5 papers · 1 filter
MatRL: Provably Generalizable Iterative Algorithm Discovery via Monte-Carlo Tree Search
Sungyoon Kim, Rajat Vadiraj Dwaraknath, Longling geng +1
Iterative methods for computing matrix functions have been extensively studied and their convergence speed can be significantly improved with the right tuning of parameters and by…
BWLer: Barycentric Weight Layer Elucidates a Precision-Conditioning Tradeoff for PINNs
Jerry Liu, Yasa Baig, Denise Hui Jean Lee +3
Physics-informed neural networks (PINNs) offer a flexible way to solve partial differential equations (PDEs) with machine learning, yet they still fall well short of the machine-pr…
SD-KDE: Score-Debiased Kernel Density Estimation
Elliot L. Epstein, Rajat Dwaraknath, Thanawat Sornwanee +2
We propose a novel method for density estimation that leverages an estimated score function to debias kernel density estimation (SD-KDE). In our approach, each data point is adjust…
Fixing the NTK: From Neural Network Linearizations to Exact Convex Programs
Rajat Vadiraj Dwaraknath, Tolga Ergen, Mert Pilanci
Recently, theoretical analyses of deep neural networks have broadly focused on two directions: 1) Providing insight into neural network training by SGD in the limit of infinite hid…
[Reproducibility Report] Rigging the Lottery: Making All Tickets Winners
Varun Sundar, Rajat Vadiraj Dwaraknath
, a sparse training algorithm, claims to directly train sparse networks that match or exceed the performance of existing dense-to-sparse training techniques (such as…