SAGE mixes attention importance with embedding diversity sampling to reach 93% of full server accuracy on ImageNet-1K while sending under half the evidence units, beating pure importance selection.
Deep learning enabled semantic communication systems
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
E2E-WAVE achieves +5 dB PSNR and real-time 16 FPS 128x128 video over 2.3 kbps underwater channels by learning waveforms that favor semantic similarity on decoding errors.
A causal diffusion model reconstructs videos from ultra-low-bitrate semantics and compressed frames using temporal distillation from a bidirectional teacher, outperforming prior baselines.
citing papers explorer
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SAGE: Training-Free Semantic Evidence Composition for Edge-Cloud Inference under Hard Uplink Budgets
SAGE mixes attention importance with embedding diversity sampling to reach 93% of full server accuracy on ImageNet-1K while sending under half the evidence units, beating pure importance selection.
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E2E-WAVE: End-to-End Learned Waveform Generation for Underwater Video Multicasting
E2E-WAVE achieves +5 dB PSNR and real-time 16 FPS 128x128 video over 2.3 kbps underwater channels by learning waveforms that favor semantic similarity on decoding errors.
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A Causal Diffusion Model for Video Reconstruction from Ultra-Low-Bitrate Representations
A causal diffusion model reconstructs videos from ultra-low-bitrate semantics and compressed frames using temporal distillation from a bidirectional teacher, outperforming prior baselines.