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Mean Flow Distillation — Robust and Stable Distillation for Flow Matching Models
Flow Matching (FM) models excel at generative tasks but suffer from slow ODE-based iterative sampling, limiting real-time use

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Flow Matching (FM) models excel at generative tasks but suffer from slow ODE-based iterative sampling, limiting real-time use

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This study addresses the challenge of differential diagnosis between asthma and COPD using acoustic analysis of multi-channel pulmonary sounds

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Piper addresses the complexity and inflexibility in current large-scale distributed training systems that combine multiple parallelism strategies (data, pipeline, expert, tensor parallelism) and me…

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This paper addresses the challenge of fault detection in distribution networks increasingly penetrated by inverter-based resources (IBRs), which produce low short-circuit fault currents that underm…

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This paper addresses the challenge of accurately modeling spatially heterogeneous power-law frequency-dependent attenuation in biological tissues for ultrasound simulations

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This paper addresses the emerging reality of the "agentic web," where users interact with online services predominantly through AI agents acting on their behalf

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This work addresses the limitation of most bioacoustic classifiers that only predict species presence in broad time windows without precisely localizing bird vocalizations in time and frequency

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This paper addresses the challenge of PET image denoising across varied dose reduction factors (DRFs), a key practical problem since existing DL methods assume a fixed DRF and degrade when the DRF …

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WorldOlympiad introduces a comprehensive benchmark to evaluate video-based world models across three critical dimensions — physical faithfulness, geometric consistency, and interaction fidelity

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This work addresses persistent challenges in bidirectional quantum key distribution (QKD) such as classical data leakage, signal-space confinement to predictable subspaces, and limited detectabilit…

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This work addresses the classical challenge of causal derivative estimation from noisy discrete-time data, fundamental in control, HCI, and biomedical domains

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This paper addresses significant challenges in robotic contact-rich manipulation that rely on force and tactile sensing