2 citations · 2 across the 3 of their papers we have counts for
4 papers
PROPS: Probabilistic personalization of black-box sequence models
Michael Thomas Wojnowicz, Xuan Zhao
We present PROPS, a lightweight transfer learning mechanism for sequential data. PROPS learns probabilistic perturbations around the predictions of one or more arbitrarily complex,…
Projecting "better than randomly": How to reduce the dimensionality of very large datasets in a way that outperforms random projections
Michael Wojnowicz, Di Zhang, Glenn Chisholm +2
For very large datasets, random projections (RP) have become the tool of choice for dimensionality reduction. This is due to the computational complexity of principal component ana…
Towards Generic Deobfuscation of Windows API Calls
Vadim Kotov, Michael Wojnowicz
A common way to get insight into a malicious program's functionality is to look at which API functions it calls. To complicate the reverse engineering of their programs, malware au…
Lazy stochastic principal component analysis
Michael Wojnowicz, Dinh Nguyen, Li Li +1
Stochastic principal component analysis (SPCA) has become a popular dimensionality reduction strategy for large, high-dimensional datasets. We derive a simplified algorithm, called…