85 citations · 114 across the 34 of their papers we have counts for
35 papers
Multi-fidelity Reinforcement Learning Control for Complex Dynamical Systems
Luning Sun, Xin-Yang Liu, Siyan Zhao +3
Controlling instabilities in complex dynamical systems is challenging in scientific and engineering applications. Deep reinforcement learning (DRL) has seen promising results for a…
Leveraging Registers in Vision Transformers for Robust Adaptation
Srikar Yellapragada, Kowshik Thopalli, Vivek Narayanaswamy +5
Vision Transformers (ViTs) have shown success across a variety of tasks due to their ability to capture global image representations. Recent studies have identified the existence o…
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 Role of Prompt Construction In Enhancing Efficacy and Efficiency of LLM-Based Tabular Data Generation
Banooqa Banday, Kowshik Thopalli, Tanzima Z. Islam +1
LLM-based data generation for real-world tabular data can be challenged by the lack of sufficient semantic context in feature names used to describe columns. We hypothesize that en…
DECIDER: Leveraging Foundation Model Priors for Improved Model Failure Detection and Explanation
Rakshith Subramanyam, Kowshik Thopalli, Vivek Narayanaswamy +1
Reliably detecting when a deployed machine learning model is likely to fail on a given input is crucial for ensuring safe operation. In this work, we propose DECIDER (Debiasing Cla…
Enhancing Accuracy and Parameter-Efficiency of Neural Representations for Network Parameterization
Hongjun Choi, Jayaraman J. Thiagarajan, Ruben Glatt +1
In this work, we investigate the fundamental trade-off regarding accuracy and parameter efficiency in the parameterization of neural network weights using predictor networks. We pr…
Speeding Up Image Classifiers with Little Companions
Yang Liu, Kowshik Thopalli, Jayaraman Thiagarajan
Scaling up neural networks has been a key recipe to the success of large language and vision models. However, in practice, up-scaled models can be disproportionately costly in term…
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…
Target-Aware Generative Augmentations for Single-Shot Adaptation
Kowshik Thopalli, Rakshith Subramanyam, Pavan Turaga +1
In this paper, we address the problem of adapting models from a source domain to a target domain, a task that has become increasingly important due to the brittle generalization of…
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…
A Closer Look at Model Adaptation using Feature Distortion and Simplicity Bias
Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan
Advances in the expressivity of pretrained models have increased interest in the design of adaptation protocols which enable safe and effective transfer learning. Going beyond conv…
On the Efficacy of Generalization Error Prediction Scoring Functions
Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan
Generalization error predictors (GEPs) aim to predict model performance on unseen distributions by deriving dataset-level error estimates from sample-level scores. However, GEPs of…
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…
Single-Shot Domain Adaptation via Target-Aware Generative Augmentation
Rakshith Subramanyam, Kowshik Thopalli, Spring Berman +2
The problem of adapting models from a source domain using data from any target domain of interest has gained prominence, thanks to the brittle generalization in deep neural network…
Analyzing Data-Centric Properties for Graph Contrastive Learning
Puja Trivedi, Ekdeep Singh Lubana, Mark Heimann +2
Recent analyses of self-supervised learning (SSL) find the following data-centric properties to be critical for learning good representations: invariance to task-irrelevant semanti…
Exploring the Design of Adaptation Protocols for Improved Generalization and Machine Learning Safety
Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan
While directly fine-tuning (FT) large-scale, pretrained models on task-specific data is well-known to induce strong in-distribution task performance, recent works have demonstrated…
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…