7 papers · 1 filter
Neural Low-Discrepancy Sequences
Michael Etienne Van Huffel, Nathan Kirk, Makram Chahine +2
Low-discrepancy points are designed to efficiently fill the space in a uniform manner. This uniformity is highly advantageous in many problems in science and engineering, including…
When Sensors Fail: Temporal Sequence Models for Robust PPO under Sensor Drift
Kevin Vogt-Lowell, Theodoros Tsiligkaridis, Rodney Lafuente-Mercado +4
Real-world reinforcement learning systems must operate under distributional drift in their observation streams, yet most policy architectures implicitly assume fully observed and n…
The Curious Case of In-Training Compression of State Space Models
Makram Chahine, Philipp Nazari, Daniela Rus +1
State Space Models (SSMs), developed to tackle long sequence modeling tasks efficiently, offer both parallelizable training and fast inference. At their core are recurrent dynamica…
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…
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…