93 citations · 153 across the 18 of their papers we have counts for
Showing 2020Show all
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cs.LG2020★ 6 cited
Optimal 1-NN Prototypes for Pathological Geometries
Ilia Sucholutsky, Matthias Schonlau
Using prototype methods to reduce the size of training datasets can drastically reduce the computational cost of classification with instance-based learning algorithms like the k-N…
cs.LG2020★ 7 cited
SecDD: Efficient and Secure Method for Remotely Training Neural Networks
Ilia Sucholutsky, Matthias Schonlau
We leverage what are typically considered the worst qualities of deep learning algorithms - high computational cost, requirement for large data, no explainability, high dependence…
cs.LG2020★ 3 cited
'Less Than One'-Shot Learning: Learning N Classes From M<N Samples
Ilia Sucholutsky, Matthias Schonlau
Deep neural networks require large training sets but suffer from high computational cost and long training times. Training on much smaller training sets while maintaining nearly th…