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cs.LG2025
No-Rank Tensor Decomposition Using Metric Learning
Maryam Bagherian
Tensor decomposition of high-dimensional data often struggles to capture semantically or physically meaningful structures, particularly when relying on reconstruction objectives an…
cs.LG2024
Universal Approximation of Operators with Transformers and Neural Integral Operators
Emanuele Zappala, Maryam Bagherian
We study the universal approximation properties of transformers and neural integral operators for operators in Banach spaces. In particular, we show that the transformer architectu…
cs.LG2022★ 1 cited
FIMP: Foundation Model-Informed Message Passing for Graph Neural Networks
Syed Asad Rizvi, Nazreen Pallikkavaliyaveetil, David Zhang +15
Foundation models have achieved remarkable success across many domains, relying on pretraining over vast amounts of data. Graph-structured data often lacks the same scale as unstru…