PhyGAIL uses bounded local graphs and physics-informed graph neural networks with gated message passing for attraction and repulsion to enable zero-shot transfer of recovery policies from 20-UAV to 500-UAV swarms.
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The capacity of distinguishable synthetic identity generation under face verification is characterized by spherical-code problems on the unit hypersphere, with lower bounds derived for both deterministic and stochastic generation models.
Scene-adaptive lattice vector quantization improves rate-distortion performance of 3DGS compression over uniform scalar quantization while adding little overhead and supporting multiple bit rates from one trained model.
citing papers explorer
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Zero-Shot Scalable Resilience in UAV Swarms: A Decentralized Imitation Learning Framework with Physics-Informed Graph Interactions
PhyGAIL uses bounded local graphs and physics-informed graph neural networks with gated message passing for attraction and repulsion to enable zero-shot transfer of recovery policies from 20-UAV to 500-UAV swarms.
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On the Capacity of Distinguishable Synthetic Identity Generation under Face Verification
The capacity of distinguishable synthetic identity generation under face verification is characterized by spherical-code problems on the unit hypersphere, with lower bounds derived for both deterministic and stochastic generation models.
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Improving 3D Gaussian Splatting Compression by Scene-Adaptive Lattice Vector Quantization
Scene-adaptive lattice vector quantization improves rate-distortion performance of 3DGS compression over uniform scalar quantization while adding little overhead and supporting multiple bit rates from one trained model.