Real-world entities are often characterized by heterogeneous structured information, including static attributes, temporal dynamics, and relational dependencies. However, existing benchmarks provide limited understanding of whether multimodal models can effectively exploit complementary information across these modalit...
Kai-Jie Cheng, Nuo-Yue Xu, Xing-Xuan Zhang et al.· AI Plus· 0 citations
Counterfactual prediction estimates an individual's outcome under an alternative intervention given their factual observations. Such outcomes are generally not identifiable from observational data without additional assumptions. Even within the class of fully observed additive noise models (ANMs), different causal grap...
Xin-Yan Han, Xiao-Yu Lin, Hao Zou et al.· 0 citations
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...
Xing-Xuan Zhang, Gang Ren, Hao Yuan et al.· 5 citations
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.
Peng Cui, Xing-Xuan Zhang, Han-Jia Ye et al.· Proceedings of the 32nd ACM...· 0 citations
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.
Chunyu Hu, Tianyin Liao, Ge Lan et al.· arXiv.org· 0 citations
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.
Peng Cui, Xingxuan Zhang, Han-Jia Ye et al.· Proceedings of the 32nd ACM...· 0 citations
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