67 citations · 238 across the 29 of their papers we have counts for
7 papers · 1 filter
Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks
Uday Shankar Shanthamallu, Jayaraman J. Thiagarajan, Andreas Spanias
Graph Neural Networks (GNNs), a generalization of neural networks to graph-structured data, are often implemented using message passes between entities of a graph. While GNNs are e…
Accurate and Robust Feature Importance Estimation under Distribution Shifts
Jayaraman J. Thiagarajan, Vivek Narayanaswamy, Rushil Anirudh +2
With increasing reliance on the outcomes of black-box models in critical applications, post-hoc explainability tools that do not require access to the model internals are often use…
Designing an Effective Metric Learning Pipeline for Speaker Diarization
Vivek Sivaraman Narayanaswamy, Jayaraman J. Thiagarajan, Huan Song +1
State-of-the-art speaker diarization systems utilize knowledge from external data, in the form of a pre-trained distance metric, to effectively determine relative speaker identitie…
GrAMME: Semi-Supervised Learning using Multi-layered Graph Attention Models
Uday Shankar Shanthamallu, Jayaraman J. Thiagarajan, Huan Song +1
Modern data analysis pipelines are becoming increasingly complex due to the presence of multi-view information sources. While graphs are effective in modeling complex relationships…
Attend and Diagnose: Clinical Time Series Analysis using Attention Models
Huan Song, Deepta Rajan, Jayaraman J. Thiagarajan +1
With widespread adoption of electronic health records, there is an increased emphasis for predictive models that can effectively deal with clinical time-series data. Powered by Rec…
Optimizing Kernel Machines using Deep Learning
Huan Song, Jayaraman J. Thiagarajan, Prasanna Sattigeri +1
Building highly non-linear and non-parametric models is central to several state-of-the-art machine learning systems. Kernel methods form an important class of techniques that indu…
A Deep Learning Approach To Multiple Kernel Fusion
Huan Song, Jayaraman J. Thiagarajan, Prasanna Sattigeri +2
Kernel fusion is a popular and effective approach for combining multiple features that characterize different aspects of data. Traditional approaches for Multiple Kernel Learning (…