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Found 4 papers, 2 papers with code
LINOCS: Lookahead Inference of Networked Operators for Continuous Stability
Such systems, often modeled as dynamical systems, typically exhibit noisy high-dimensional and non-stationary temporal behavior that renders their identification challenging.
Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics
Learning interpretable representations of neural dynamics at a population level is a crucial first step to understanding how observed neural activity relates to perception and behavior.
Prospective Learning: Principled Extrapolation to the Future
We conjecture that certain sequences of tasks are not retrospectively learnable (in which the data distribution is fixed), but are prospectively learnable (in which distributions may be dynamic), suggesting that prospective learning is more difficult in kind than retrospective learning.
Random Forests for Adaptive Nearest Neighbor Estimation of Information-Theoretic Quantities
Information-theoretic quantities, such as conditional entropy and mutual information, are critical data summaries for quantifying uncertainty.
