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Compression schemes are a general framework for deriving data-dependent risk bounds for learning algorithms; the core idea is that if a learning algorithm can compress and reconstruct a hypothesis using a small subset of training samples, this compression capability implies generalization guarantees.
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10/12/2026
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UnverifiedSpeculative Decoding is an inference optimization technique that uses a small model to draft and a large model to verify, while quantization compression reduces model weights from 16-bit floating point to 4-bit or 8-bit integers.72% similarUnverifiedExisting context compression approaches typically have the model itself perform the compression process, causing the timing, granularity, and information trade-offs of compression to depend on the model's real-time judgment, which both consumes inference resources and is difficult to audit.70% similarUnverifiedCompression and prediction are essentially the same thing; a model that compresses data well must have a good ability to model the data distribution.70% similarVerified模型压缩涵盖量化、剪枝、知识蒸馏三条主要技术路径69% similarUnverifiedCombining model compression techniques can reduce model size by 5–10x and improve inference speed by 2–4x.68% similar
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