activity
20192022
most citedSecDD: Efficient and Secure Method for Remotely Training Neural Networks

7 citations · 22 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG20226 cited

Predicting Human Similarity Judgments Using Large Language Models

Raja Marjieh, Ilia Sucholutsky, Theodore R. Sumers +2

Similarity judgments provide a well-established method for accessing mental representations, with applications in psychology, neuroscience and machine learning. However, collecting…

cs.LG20223 cited

Can Humans Do Less-Than-One-Shot Learning?

Maya Malaviya, Ilia Sucholutsky, Kerem Oktar +1

Being able to learn from small amounts of data is a key characteristic of human intelligence, but exactly {\em how} small? In this paper, we introduce a novel experimental paradigm…

cs.LG20206 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.LG20207 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…