93 citations · 148 across the 8 of their papers we have counts for
6 papers · 1 filter
One Line To Rule Them All: Generating LO-Shot Soft-Label Prototypes
Ilia Sucholutsky, Nam-Hwui Kim, Ryan P. Browne +1
Increasingly large datasets are rapidly driving up the computational costs of machine learning. Prototype generation methods aim to create a small set of synthetic observations tha…
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…
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…
'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…
Soft-Label Dataset Distillation and Text Dataset Distillation
Ilia Sucholutsky, Matthias Schonlau
Dataset distillation is a method for reducing dataset sizes by learning a small number of synthetic samples containing all the information of a large dataset. This has several bene…
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…