PoseFM is the first method to reformulate monocular frame-to-frame visual odometry as a flow-matching generative model that predicts camera pose distributions for built-in uncertainty.
Score- based generative modeling through stochastic differential equations
7 Pith papers cite this work. Polarity classification is still indexing.
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Reconstruction of global 1.5 km resolution 10 m vector winds in tropical cyclone inner cores from sparse CYGNSS scalar observations using generalized score-based diffusion assimilation plus three TC boundary-layer constraints.
Derives optimal inference-time guidance for stochastic interpolant policies via Kolmogorov equation analysis, enabling reactive streaming robot control with training-free and training-based mechanisms.
CARD uses radix decomposition to enable autoregressive modeling of molecular coordinates as a zero-free-energy reference distribution, delivering classical accuracy for absolute free energy on unseen systems at ~40x speedup.
TRFP combines rectified flow models with truncation to support multimodal policies in MaxEnt RL while allowing fast one-step sampling and stable training.
The paper introduces Hyper Diffusion Planner (HDP), a diffusion-based E2E AD framework that identifies insights on loss space, trajectory representation and data scaling, adds RL post-training, and reports 10x performance gains over 200 km of real-world testing across 6 scenarios.
A latent diffusion model jointly synthesizes MRI volumes and mixed-type tabular clinical data in a shared space via cross-attention and separate decoders after VAE fusion.
citing papers explorer
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PoseFM: Relative Camera Pose Estimation Through Flow Matching
PoseFM is the first method to reformulate monocular frame-to-frame visual odometry as a flow-matching generative model that predicts camera pose distributions for built-in uncertainty.
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Global kilometre-scale tropical cyclone inner-core vector winds from sparse scalar CYGNSS observations
Reconstruction of global 1.5 km resolution 10 m vector winds in tropical cyclone inner cores from sparse CYGNSS scalar observations using generalized score-based diffusion assimilation plus three TC boundary-layer constraints.
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Guided Streaming Stochastic Interpolant Policy
Derives optimal inference-time guidance for stochastic interpolant policies via Kolmogorov equation analysis, enabling reactive streaming robot control with training-free and training-based mechanisms.
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CARD: Coarse-to-fine Autoregressive Modeling with Radix-based Decomposition for Transferable Free Energy Estimation
CARD uses radix decomposition to enable autoregressive modeling of molecular coordinates as a zero-free-energy reference distribution, delivering classical accuracy for absolute free energy on unseen systems at ~40x speedup.
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Truncated Rectified Flow Policy for Reinforcement Learning with One-Step Sampling
TRFP combines rectified flow models with truncation to support multimodal policies in MaxEnt RL while allowing fast one-step sampling and stable training.
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Unleashing the Potential of Diffusion Models for End-to-End Autonomous Driving
The paper introduces Hyper Diffusion Planner (HDP), a diffusion-based E2E AD framework that identifies insights on loss space, trajectory representation and data scaling, adds RL post-training, and reports 10x performance gains over 200 km of real-world testing across 6 scenarios.
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Multimodal synthesis of MRI and tabular data with diffusion in a joint latent space via cross-attention
A latent diffusion model jointly synthesizes MRI volumes and mixed-type tabular clinical data in a shared space via cross-attention and separate decoders after VAE fusion.