AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This paper presents the first polynomial-time algorithm for learning with exact group invariances that applies uniformly to finite and infinite groups, and shows that exact symmetries can be identified from data and exploited for learning in polynomial time.
In nonconvex optimization problems arising in geometric machine learning, data augmentation is commonly used to promote invariance by averaging empirical losses over transformations of the data. Computing the fully augmented objective, however, requires access to every element of the transformation group $G$, which may...
This work studies adaptive symmetry discovery for dynamical system identification and addresses how a system can be identified from a single trajectory when it is equivariant with respect to an unknown symmetry group.
GCC formulates coded computing through a natural end-to-end mean-squared error loss that directly measures the discrepancy between the desired computations and their recovered estimates, and enables theoretical performance guarantees for GCC under two complementary straggler regimes.
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