LLM embeddings condition a generative transformer to enable faster convergence, better performance, and generalization to unseen LHC processes using a single model.
uller, David I. and Schuh, Daniel , title =
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Lie groupoid equivariant CNNs are defined via lifting and convolution layers that specialize category-equivariant networks, shown equivalent to algebroid versions for suitable groupoids and as special cases of admissible category-equivariant layers via continuous natural transformations.
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One Generator, Any Process: LLM-Conditioning for the LHC
LLM embeddings condition a generative transformer to enable faster convergence, better performance, and generalization to unseen LHC processes using a single model.
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Theoretical Aspects of Lie Groupoid and Lie Algebroid Equivariant Convolutional Neural Networks
Lie groupoid equivariant CNNs are defined via lifting and convolution layers that specialize category-equivariant networks, shown equivalent to algebroid versions for suitable groupoids and as special cases of admissible category-equivariant layers via continuous natural transformations.