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cs.LG2024★ 3 cited
DyCE: Dynamically Configurable Exiting for Deep Learning Compression and Real-time Scaling
Qingyuan Wang, Barry Cardiff, Antoine Frappé +2
Conventional deep learning (DL) model compression and scaling methods focus on altering the model's components, impacting the results across all samples uniformly. However, since s…
cs.LG2023
POCKET: Pruning Random Convolution Kernels for Time Series Classification from a Feature Selection Perspective
Shaowu Chen, Weize Sun, Lei Huang +3
In recent years, two competitive time series classification models, namely, ROCKET and MINIROCKET, have garnered considerable attention due to their low training cost and high accu…