AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Results show that recurrent token mixing can provide an efficient alternative to global softmax attention for high-resolution dense prediction and edge deployment, and are shown to be faster than FLA v0.5.
This work investigates a simple yet effective conditional generative framework for text-to-motion generation and proposes T2M-GIT+, which employs a non-autoregressive method to generate discrete motion representations in parallel, and is therefore more efficient than T2M-GPT+ while achieving comparable results.
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