AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work proposes an efficient strategy that enables merging of layers without an existing analytical solution, and also without increasing kernel size, and validate the approach across multiple architectures and datasets, and measure inference speed-up gains on real embedded platforms.
This work introduces TORINO (TOken Reduction via Interpretable coNcept Overlap), a plug-and-play framework for adaptive visual token reduction in VLMs that requires no fine-tuning of the underlying model.
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