19 citations · 34 across the 20 of their papers we have counts for
3 papers · 1 filter
IT-RUDA: Information Theory Assisted Robust Unsupervised Domain Adaptation
Shima Rashidi, Ruwan Tennakoon, Aref Miri Rekavandi +7
Distribution shift between train (source) and test (target) datasets is a common problem encountered in machine learning applications. One approach to resolve this issue is to use…
Monotone Boolean Functions, Feasibility/Infeasibility, LP-type problems and MaxCon
David Suter, Ruwan Tennakoon, Erchuan Zhang +2
This paper outlines connections between Monotone Boolean Functions, LP-Type problems and the Maximum Consensus Problem. The latter refers to a particular type of robust fitting cha…
nuts-flow/ml: data pre-processing for deep learning
S. Maetschke, R. Tennakoon, C. Vecchiola +1
Data preprocessing is a fundamental part of any machine learning application and frequently the most time-consuming aspect when developing a machine learning solution. Preprocessin…