Titlebook: Machine Learning and Knowledge Discovery in Databases. Research Track; European Conference, Nuria Oliver,Fernando Pérez-Cruz,Jose A. Lozano

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Asparagus

amygdala

Joslim: ,oint Widths and Weights ,ptimization for ,mable Neural Networks. From a practical standpoint, we propose Joslim, an algorithm that jointly optimizes both the widths and weights for slimmable nets, which outperforms existing methods for optimizing slimmable networks across various networks, datasets, and objectives. Quantitatively, improvements up?to 1.7% and 8%

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lavish

cravat

講個(gè)故事逗他

Variance Reduced Stochastic Proximal Algorithm for AUC Maximization Variance Reduced Stochastic Proximal algorithm for AUC Maximization (.) that combines the two areas of analyzing non-decomposable performance metrics with and optimization efforts to guarantee faster convergence. We perform an in-depth theoretical and empirical analysis to demonstrate that our algo

Interregnum

More General and Effective Model Compression via an Additive Combination of Compressionsusing only 1 bit per weight without error degradation at the cost of adding a few floating point weights. However, VGG nets can be better compressed by combining low-rank with a few floating point weights.

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