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English(EN) EV-JEPA: Elastic V-JEPA for Any Device and Cross-Device Inference

弹性V-JEPA模型实现自适应跨设备AI推理

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出Elastic V-JEPA (EV-JEPA)，这是一种新颖的基础模型，旨在部署于从智能眼镜到服务器的各种设备上。与为特定硬件训练的传统模型不同，EV-JEPA经过一次训练即可适应其计算预算，用于单设备或协作式跨设备推理。这种灵活性是通过Transition-Aware Triplet Training (T3)实现的，该方法使模型能够学习狭窄和宽泛的配置，以及用于设备切换的混合路径。EV-JEPA在性能上接近独立训练的模型，同时显著优于现有的弹性训练方法。 AI

影响 实现了基础模型在不同硬件上更灵活、更高效的部署，可能降低推理成本和延迟。

排序理由 这是一篇描述新模型架构和训练方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

弹性V-JEPA模型实现自适应跨设备AI推理

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这是一篇描述新模型架构和训练方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Anxhelo Diko, Jieyu Lin, Stefano Ambrogio, Ziyun Li, Mahmoud Assran, Th\'eo Moutakanni, Barbara De Salvo, Chiao Liu ·

    EV-JEPA：适用于任何设备和跨设备推理的弹性V-JEPA

    arXiv:2610.14324v1 Announce Type: cross Abstract: Foundation models like V-JEPA are increasingly deployed across a heterogeneous edge-cloud continuum spanning smart glasses, phones, robots, and servers. Yet, they are typically trained as rigid, fixed-capacity networks bound to on…

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