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Showing 2020Show all
3 papers · 1 filter
cs.LG2020★ 1 cited
Dirichlet Pruning for Neural Network Compression
Kamil Adamczewski, Mijung Park
We introduce Dirichlet pruning, a novel post-processing technique to transform a large neural network model into a compressed one. Dirichlet pruning is a form of structured pruning…
cs.DS2020
Group-Harmonic and Group-Closeness Maximization -- Approximation and Engineering
Eugenio Angriman, Ruben Becker, Gianlorenzo D'Angelo +3
Centrality measures characterize important nodes in networks. Efficiently computing such nodes has received a lot of attention. When considering the generalization of computing cen…
cs.CV2020
Exact MAP-Inference by Confining Combinatorial Search with LP Relaxation
Stefan Haller, Paul Swoboda, Bogdan Savchynskyy
We consider the MAP-inference problem for graphical models, which is a valued constraint satisfaction problem defined on real numbers with a natural summation operation. We propose…