62 citations · 177 across the 45 of their papers we have counts for
54 papers
Physics-Informed Transformation Toward Improving the Machine-Learned NLTE Models of ICF Simulations
Min Sang Cho, Paul E. Grabowski, Kowshik Thopalli +11
The integration of machine learning techniques into Inertial Confinement Fusion (ICF) simulations has emerged as a powerful approach for enhancing computational efficiency. By repl…
On the Use of Anchoring for Training Vision Models
Vivek Narayanaswamy, Kowshik Thopalli, Rushil Anirudh +3
Anchoring is a recent, architecture-agnostic principle for training deep neural networks that has been shown to significantly improve uncertainty estimation, calibration, and extra…
`Eyes of a Hawk and Ears of a Fox': Part Prototype Network for Generalized Zero-Shot Learning
Joshua Feinglass, Jayaraman J. Thiagarajan, Rushil Anirudh +2
Current approaches in Generalized Zero-Shot Learning (GZSL) are built upon base models which consider only a single class attribute vector representation over the entire image. Thi…
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks
Puja Trivedi, Mark Heimann, Rushil Anirudh +2
While graph neural networks (GNNs) are widely used for node and graph representation learning tasks, the reliability of GNN uncertainty estimates under distribution shifts remains…
Transformer-Powered Surrogates Close the ICF Simulation-Experiment Gap with Extremely Limited Data
Matthew L. Olson, Shusen Liu, Jayaraman J. Thiagarajan +3
Recent advances in machine learning, specifically transformer architecture, have led to significant advancements in commercial domains. These powerful models have demonstrated supe…
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks
Puja Trivedi, Mark Heimann, Rushil Anirudh +2
Safe deployment of graph neural networks (GNNs) under distribution shift requires models to provide accurate confidence indicators (CI). However, while it is well-known in computer…
PAGER: A Framework for Failure Analysis of Deep Regression Models
Jayaraman J. Thiagarajan, Vivek Narayanaswamy, Puja Trivedi +1
Safe deployment of AI models requires proactive detection of failures to prevent costly errors. To this end, we study the important problem of detecting failures in deep regression…
CREPE: Learnable Prompting With CLIP Improves Visual Relationship Prediction
Rakshith Subramanyam, T. S. Jayram, Rushil Anirudh +1
In this paper, we explore the potential of Vision-Language Models (VLMs), specifically CLIP, in predicting visual object relationships, which involves interpreting visual features…
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…
DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction
Jiaming Liu, Rushil Anirudh, Jayaraman J. Thiagarajan +4
Limited-Angle Computed Tomography (LACT) is a non-destructive evaluation technique used in a variety of applications ranging from security to medicine. The limited angle coverage i…
On-the-fly Object Detection using StyleGAN with CLIP Guidance
Yuzhe Lu, Shusen Liu, Jayaraman J. Thiagarajan +2
We present a fully automated framework for building object detectors on satellite imagery without requiring any human annotation or intervention. We achieve this by leveraging the…
Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification
Rakshith Subramanyam, Mark Heimann, Jayram Thathachar +2
Model agnostic meta-learning algorithms aim to infer priors from several observed tasks that can then be used to adapt to a new task with few examples. Given the inherent diversity…
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…
Know Your Space: Inlier and Outlier Construction for Calibrating Medical OOD Detectors
Vivek Narayanaswamy, Yamen Mubarka, Rushil Anirudh +3
We focus on the problem of producing well-calibrated out-of-distribution (OOD) detectors, in order to enable safe deployment of medical image classifiers. Motivated by the difficul…
Out of Distribution Detection via Neural Network Anchoring
Rushil Anirudh, Jayaraman J. Thiagarajan
Our goal in this paper is to exploit heteroscedastic temperature scaling as a calibration strategy for out of distribution (OOD) detection. Heteroscedasticity here refers to the fa…
Improving Diversity with Adversarially Learned Transformations for Domain Generalization
Tejas Gokhale, Rushil Anirudh, Jayaraman J. Thiagarajan +3
To be successful in single source domain generalization, maximizing diversity of synthesized domains has emerged as one of the most effective strategies. Many of the recent success…
Revisiting Deep Subspace Alignment for Unsupervised Domain Adaptation
Kowshik Thopalli, Jayaraman J Thiagarajan, Rushil Anirudh +1
Unsupervised domain adaptation (UDA) aims to transfer and adapt knowledge from a labeled source domain to an unlabeled target domain. Traditionally, subspace-based methods form an…
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…
-UQ: Accurate Uncertainty Quantification via Anchor Marginalization
Rushil Anirudh, Jayaraman J. Thiagarajan
We present -UQ -- a novel, general-purpose uncertainty estimator using the concept of anchoring in predictive models. Anchoring works by first transforming the input into a tupl…
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…
Recovering Trajectories of Unmarked Joints in 3D Human Actions Using Latent Space Optimization
Suhas Lohit, Rushil Anirudh, Pavan Turaga
Motion capture (mocap) and time-of-flight based sensing of human actions are becoming increasingly popular modalities to perform robust activity analysis. Applications range from a…
Attribute-Guided Adversarial Training for Robustness to Natural Perturbations
Tejas Gokhale, Rushil Anirudh, Bhavya Kailkhura +3
While existing work in robust deep learning has focused on small pixel-level norm-based perturbations, this may not account for perturbations encountered in several real-world sett…
Meaningful uncertainties from deep neural network surrogates of large-scale numerical simulations
Gemma J. Anderson, Jim A. Gaffney, Brian K. Spears +3
Large-scale numerical simulations are used across many scientific disciplines to facilitate experimental development and provide insights into underlying physical processes, but th…
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…