Search Results for author:
Found 6 papers, 4 papers with code
Improved Automated Machine Learning from Transfer Learning
In this paper, we propose a neural architecture search framework based on a similarity measure between some baseline tasks and a target task.
Task-Aware Neural Architecture Search
The design of handcrafted neural networks requires a lot of time and resources.
Approximating the Riemannian Metric from Point Clouds via Manifold Moving Least Squares
In this paper, we investigate the convergence of such approximations made by Manifold Moving Least-Squares (Manifold-MLS), a method that constructs an approximating manifold $\mathcal{M}^h$ using information from a given point cloud that was developed by Sober \& Levin in 2019.
Learning Partial Differential Equations from Data Using Neural Networks
We develop a framework for estimating unknown partial differential equations from noisy data, using a deep learning approach.
Quantifying Gerrymandering in North Carolina
Using an ensemble of redistricting plans, we evaluate whether a given political districting faithfully represents the geo-political landscape.
Physics and Society Applications
Redistricting: Drawing the Line
To explore and showcase our ideas, we concentrate on the congressional districts for the U. S. House of representatives and use the state of North Carolina and its redistrictings since the 2010 census.
Applications 91F10 G.3; K.4.1
