1 citations · 1 across the 2 of their papers we have counts for
3 papers
Quantifying Memory Use in Reinforcement Learning with Temporal Range
Rodney Lafuente-Mercado, Daniela Rus, T. Konstantin Rusch
How much does a trained RL policy actually use its past observations? We propose \emph{Temporal Range}, a model-agnostic metric that treats first-order sensitivities of multiple ve…
Learning to Dissipate Energy in Oscillatory State-Space Models
Jared Boyer, T. Konstantin Rusch, Daniela Rus
State-space models (SSMs) are a class of networks for sequence learning that benefit from fixed state size and linear complexity with respect to sequence length, contrasting the qu…
Low Stein Discrepancy via Message-Passing Monte Carlo
Nathan Kirk, T. Konstantin Rusch, Jakob Zech +1
Message-Passing Monte Carlo (MPMC) was recently introduced as a novel low-discrepancy sampling approach leveraging tools from geometric deep learning. While originally designed for…