5 citations · 8 across the 10 of their papers we have counts for
3 papers · 1 filter
Oscillatory State-Space Models
T. Konstantin Rusch, Daniela Rus
We propose Linear Oscillatory State-Space models (LinOSS) for efficiently learning on long sequences. Inspired by cortical dynamics of biological neural networks, we base our propo…
Improving Efficiency of Sampling-based Motion Planning via Message-Passing Monte Carlo
Makram Chahine, T. Konstantin Rusch, Zach J. Patterson +1
Sampling-based motion planning methods, while effective in high-dimensional spaces, often suffer from inefficiencies due to irregular sampling distributions, leading to suboptimal…
Message-Passing Monte Carlo: Generating low-discrepancy point sets via Graph Neural Networks
T. Konstantin Rusch, Nathan Kirk, Michael M. Bronstein +2
Discrepancy is a well-known measure for the irregularity of the distribution of a point set. Point sets with small discrepancy are called low-discrepancy and are known to efficient…