AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
We have 2 of 78 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
This work proposes RTA, a simple MLP-based framework that replaces structural message passing with label-aware retrieval and propagation and provides theoretical insights that connect retrieval-based aggregation to softmax-attention message passing and establish the robustness of retrieved-context supervision to mis-retrieved outliers.
Trend-aware Pruning is proposed, a novel framework that elevates pruning from a local snapshot decision to a temporal trajectory modeling problem, and enables a dynamic rectification mechanism that selectively reactivates "late-blooming" tokens, those initially undervalued but exhibiting rising semantic importance, thereby preventing the loss of critical visual cues.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.