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Muckley, Ricky T

4 Pith papers cite this work. Polarity classification is still indexing.

4 Pith papers citing it

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UNVERDICTED 4

representative citing papers

FlowADMM: Plug-and-play ADMM with Flow-based Renoise-Denoise Priors

cs.CV · 2026-05-09 · unverdicted · novelty 7.0

FlowADMM replaces stochastic renoise-denoise steps in flow-based plug-and-play methods with a deterministic expectation operator inside ADMM, yielding convergence guarantees under weak Lipschitz conditions and state-of-the-art results on standard inverse problems.

Real-Time Execution of Action Chunking Flow Policies

cs.RO · 2025-06-09 · unverdicted · novelty 6.0

Real-time chunking (RTC) allows diffusion- and flow-based action chunking policies to execute smoothly and asynchronously, maintaining high success rates on dynamic tasks even with significant inference latency.

Flow Matching Guide and Code

cs.LG · 2024-12-09 · unverdicted · novelty 2.0

Flow Matching is a generative modeling framework with mathematical foundations, design choices, extensions, and open-source PyTorch code for applications like image and text generation.

citing papers explorer

Showing 4 of 4 citing papers.

  • FlowADMM: Plug-and-play ADMM with Flow-based Renoise-Denoise Priors cs.CV · 2026-05-09 · unverdicted · none · ref 26

    FlowADMM replaces stochastic renoise-denoise steps in flow-based plug-and-play methods with a deterministic expectation operator inside ADMM, yielding convergence guarantees under weak Lipschitz conditions and state-of-the-art results on standard inverse problems.

  • Real-Time Execution of Action Chunking Flow Policies cs.RO · 2025-06-09 · unverdicted · none · ref 48

    Real-time chunking (RTC) allows diffusion- and flow-based action chunking policies to execute smoothly and asynchronously, maintaining high success rates on dynamic tasks even with significant inference latency.

  • Understanding Asynchronous Inference Methods for Vision-Language-Action Models cs.RO · 2026-05-04 · unverdicted · none · ref 15

    Controlled benchmarks show per-step residual correction (A2C2) as most effective for VLA asynchronous inference up to d=8 delays on Kinetix with over 90% solve rate, outperforming inpainting and conditioning while training-based simulation is most robust.

  • Flow Matching Guide and Code cs.LG · 2024-12-09 · unverdicted · none · ref 64

    Flow Matching is a generative modeling framework with mathematical foundations, design choices, extensions, and open-source PyTorch code for applications like image and text generation.