2 papers
cs.LG2026
In-Context Semi-Supervised Learning
Jiashuo Fan, Paul Rosu, Aaron T. Wang +3
There has been significant recent interest in understanding the capacity of Transformers for in-context learning (ICL), yet most theory focuses on supervised settings with explicit…
cs.LG2025
Graph Transformers Dream of Electric Flow
Xiang Cheng, Lawrence Carin, Suvrit Sra
We show theoretically and empirically that the linear Transformer, when applied to graph data, can implement algorithms that solve canonical problems such as electric flow and eige…