AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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We introduce LimiX-2, a new model in the LimiX family, developed through model and data scaling guided by our previously established scaling laws. LimiX-2 adopts the Contextual Mechanism Networks (CMNs) paradigm and is pretrained with Context-Conditional Masked Modeling (CCMM). CMNs shifts the organizing principle of i...
Structured data such as tabular data, time series and graphs powers many core data mining applications including recommendation, forecasting and user behavior analysis. Conventional approaches such as statistical models, classical machine learning methods and deep neural networks have achieved strong results. Yet most...
GTAlign is proposed, a surprisingly simple yet effective Graph-to-Table Alignment framework for text-free Graph Foundation Model, and a community-guided continual pre-training, where pseudo-labels derived from graph community are used to construct few-shot prediction episodes.
This tutorial presents a systematic overview of this emerging paradigm of tabular foundation models, which treats tables as a common representation that can capture information from tabular data, time series, and graphs within a shared learning framework.
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