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Tamara von Glehn

2 accepted papers

2024

Position: Categorical Deep Learning is an Algebraic Theory of All Architectures

ICML 2024poster

We present our position on the elusive quest for a general-purpose framework for specifying and studying deep learning architectures. Our opinion is that the key attempts made so far lack a coherent bridge between specifying constraints which models must satisfy and specifying their implementations.…

Cited by 35SourcePDFScholar
2021

Grounded Language Learning Fast and Slow

ICLR 2021spotlight

Recent work has shown that large text-based neural language models acquire a surprising propensity for one-shot learning. Here, we show that an agent situated in a simulated 3D world, and endowed with a novel dual-coding external memory, can exhibit similar one-shot word learning when trained with c…