85 citations · 202 across the 44 of their papers we have counts for
12 papers · 1 filter
Cross-GAN Auditing: Unsupervised Identification of Attribute Level Similarities and Differences between Pretrained Generative Models
Matthew L. Olson, Shusen Liu, Rushil Anirudh +3
Generative Adversarial Networks (GANs) are notoriously difficult to train especially for complex distributions and with limited data. This has driven the need for tools to audit tr…
Models Out of Line: A Fourier Lens on Distribution Shift Robustness
Sara Fridovich-Keil, Brian R. Bartoldson, James Diffenderfer +2
Improving the accuracy of deep neural networks (DNNs) on out-of-distribution (OOD) data is critical to an acceptance of deep learning (DL) in real world applications. It has been o…
Single Model Uncertainty Estimation via Stochastic Data Centering
Jayaraman J. Thiagarajan, Rushil Anirudh, Vivek Narayanaswamy +1
We are interested in estimating the uncertainties of deep neural networks, which play an important role in many scientific and engineering problems. In this paper, we present a str…
Geometric Priors for Scientific Generative Models in Inertial Confinement Fusion
Ankita Shukla, Rushil Anirudh, Eugene Kur +5
In this paper, we develop a Wasserstein autoencoder (WAE) with a hyperspherical prior for multimodal data in the application of inertial confinement fusion. Unlike a typical hypers…
Suppressing simulation bias using multi-modal data
Bogdan Kustowski, Jim A. Gaffney, Brian K. Spears +6
Many problems in science and engineering require making predictions based on few observations. To build a robust predictive model, these sparse data may need to be augmented with s…
Machine Learning-Powered Mitigation Policy Optimization in Epidemiological Models
Jayaraman J. Thiagarajan, Peer-Timo Bremer, Rushil Anirudh +3
A crucial aspect of managing a public health crisis is to effectively balance prevention and mitigation strategies, while taking their socio-economic impact into account. In partic…
Accurate Calibration of Agent-based Epidemiological Models with Neural Network Surrogates
Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer +3
Calibrating complex epidemiological models to observed data is a crucial step to provide both insights into the current disease dynamics, i.e.\ by estimating a reproductive number,…
Improved Surrogates in Inertial Confinement Fusion with Manifold and Cycle Consistencies
Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer +1
Neural networks have become very popular in surrogate modeling because of their ability to characterize arbitrary, high dimensional functions in a data driven fashion. This paper a…
Function Preserving Projection for Scalable Exploration of High-Dimensional Data
Shusen Liu, Rushil Anirudh, Jayaraman J. Thiagarajan +1
We present function preserving projections (FPP), a scalable linear projection technique for discovering interpretable relationships in high-dimensional data. Conventional dimensio…
Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications
Shusen Liu, Di Wang, Dan Maljovec +13
With the rapid adoption of machine learning techniques for large-scale applications in science and engineering comes the convergence of two grand challenges in visualization. First…
A Look at the Effect of Sample Design on Generalization through the Lens of Spectral Analysis
Bhavya Kailkhura, Jayaraman J. Thiagarajan, Qunwei Li +1
This paper provides a general framework to study the effect of sampling properties of training data on the generalization error of the learned machine learning (ML) models. Specifi…
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…