Quantized local reduced-order models paired with adjoint optimization reconstruct full trajectories in the chaotic Kuramoto-Sivashinsky equation up to 0.25 Lyapunov times with 3.5x speedup over full-order models.
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Cognitive Flexibility is a new representation-level operator for Bayesian filters that dynamically selects latent structures via predictive scores to reduce inconsistency under mismatch while preserving the recursion and exhibiting descent and finite-switching properties.
Derives a contact-aware Fisher information measure to synthesize robot behaviors that maximize information-rich contacts for efficient object parameter learning.
citing papers explorer
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Adjoint-based optimization with quantized local reduced-order models for spatiotemporally chaotic systems
Quantized local reduced-order models paired with adjoint optimization reconstruct full trajectories in the chaotic Kuramoto-Sivashinsky equation up to 0.25 Lyapunov times with 3.5x speedup over full-order models.
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Cognitive Flexibility as a Latent Structural Operator for Bayesian State Estimation
Cognitive Flexibility is a new representation-level operator for Bayesian filters that dynamically selects latent structures via predictive scores to reduce inconsistency under mismatch while preserving the recursion and exhibiting descent and finite-switching properties.
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Behavior Synthesis via Contact-Aware Fisher Information Maximization
Derives a contact-aware Fisher information measure to synthesize robot behaviors that maximize information-rich contacts for efficient object parameter learning.