OTF decomposes transitions into reusable primitives to form action-like latents in OTF-LAM and OTF-LAM-Dino, enabling zeroshot transfer and competitive policy learning under visual ambiguity.
Horn and Brian G
4 Pith papers cite this work. Polarity classification is still indexing.
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A video-to-PDE pipeline extracts the model u_t + v(t)·∇u = 9.005|∇u|^2 + 0.666Δu from grayscale ink-plume footage, outperforming advection-diffusion baselines on held-out frames and reducing to linear form via Cole-Hopf transformation.
GeoIMO uses a yaw-compensated focus of expansion model on event streams to classify independent object motion via scale-invariant residuals without training or labels.
NL-RMM-GKS extends majorization-minimization and Krylov subspace recycling to nonlinear inverse problems with uncertain forward operators, offering alternating minimization, variable projection, and streaming variants for dynamic imaging.
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Latent Actions from Factorized Transition Effects under Agent Ambiguity
OTF decomposes transitions into reusable primitives to form action-like latents in OTF-LAM and OTF-LAM-Dino, enabling zeroshot transfer and competitive policy learning under visual ambiguity.