6 citations · 20 across the 12 of their papers we have counts for
6 papers · 1 filter
Designing Counterfactual Generators using Deep Model Inversion
Jayaraman J. Thiagarajan, Vivek Narayanaswamy, Deepta Rajan +3
Explanation techniques that synthesize small, interpretable changes to a given image while producing desired changes in the model prediction have become popular for introspecting b…
Loss Estimators Improve Model Generalization
Vivek Narayanaswamy, Jayaraman J. Thiagarajan, Deepta Rajan +1
With increased interest in adopting AI methods for clinical diagnosis, a vital step towards safe deployment of such tools is to ensure that the models not only produce accurate pre…
Using Deep Image Priors to Generate Counterfactual Explanations
Vivek Narayanaswamy, Jayaraman J. Thiagarajan, Andreas Spanias
Through the use of carefully tailored convolutional neural network architectures, a deep image prior (DIP) can be used to obtain pre-images from latent representation encodings. Th…
Audio Source Separation via Multi-Scale Learning with Dilated Dense U-Nets
Vivek Sivaraman Narayanaswamy, Sameeksha Katoch, Jayaraman J. Thiagarajan +2
Modern audio source separation techniques rely on optimizing sequence model architectures such as, 1D-CNNs, on mixture recordings to generalize well to unseen mixtures. Specificall…
A Regularized Attention Mechanism for Graph Attention Networks
Uday Shankar Shanthamallu, Jayaraman J. Thiagarajan, Andreas Spanias
Machine learning models that can exploit the inherent structure in data have gained prominence. In particular, there is a surge in deep learning solutions for graph-structured data…
Coverage-Based Designs Improve Sample Mining and Hyper-Parameter Optimization
Gowtham Muniraju, Bhavya Kailkhura, Jayaraman J. Thiagarajan +3
Sampling one or more effective solutions from large search spaces is a recurring idea in machine learning, and sequential optimization has become a popular solution. Typical exampl…