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Found 3 papers, 1 papers with code
PruMUX: Augmenting Data Multiplexing with Model Compression
As language models increase in size by the day, methods for efficient inference are critical to leveraging their capabilities for various applications.
Self-Renormalization of Quasi-Light-Front Correlators on the Lattice
In applying large-momentum effective theory, renormalization of the Euclidean correlators in lattice regularization is a challenge due to linear divergences in the self-energy of Wilson lines.
High Energy Physics - Lattice High Energy Physics - Phenomenology
Privacy-preserving Learning via Deep Net Pruning
This paper attempts to answer the question whether neural network pruning can be used as a tool to achieve differential privacy without losing much data utility.
