7 citations · 13 across the 2 of their papers we have counts for
3 papers
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.LG2019
Deep Learning for System Trace Restoration
Ilia Sucholutsky, Apurva Narayan, Matthias Schonlau +1
Most real-world datasets, and particularly those collected from physical systems, are full of noise, packet loss, and other imperfections. However, most specification mining, anoma…