75 citations · 93 across the 17 of their papers we have counts for
17 papers
Cyber-Physical Digital Factory Architecture as the Enabler of Disembodied Work
Tero Kaarlela, Ivan Ruchkin, Jose Outeiro +1
Digital Twins (DTs), Artificial Intelligence (AI), and Industrial Internet of Things (IIoT) technologies have significantly advanced manufacturing digitalization. However, these te…
Revisiting KRISP: A Lightweight Reproduction and Analysis of Knowledge-Enhanced Vision-Language Models
Souradeep Dutta, Keshav Bulia, Neena S Nair
Facebook AI Research introduced KRISP [4], which integrates structured external knowledge into pipelines for vision-language reasoning. Despite its effectiveness, the original mode…
RICL: Adding In-Context Adaptability to Pre-Trained Vision-Language-Action Models
Kaustubh Sridhar, Souradeep Dutta, Dinesh Jayaraman +1
Multi-task ``vision-language-action'' (VLA) models have recently demonstrated increasing promise as generalist foundation models for robotics, achieving non-trivial performance out…
Safety Monitoring for Learning-Enabled Cyber-Physical Systems in Out-of-Distribution Scenarios
Vivian Lin, Ramneet Kaur, Yahan Yang +6
The safety of learning-enabled cyber-physical systems is compromised by the well-known vulnerabilities of deep neural networks to out-of-distribution (OOD) inputs. Existing literat…
REGENT: A Retrieval-Augmented Generalist Agent That Can Act In-Context in New Environments
Kaustubh Sridhar, Souradeep Dutta, Dinesh Jayaraman +1
Building generalist agents that can rapidly adapt to new environments is a key challenge for deploying AI in the digital and real worlds. Is scaling current agent architectures the…
Automating Weak Label Generation for Data Programming with Clinicians in the Loop
Jean Park, Sydney Pugh, Kaustubh Sridhar +7
Large Deep Neural Networks (DNNs) are often data hungry and need high-quality labeled data in copious amounts for learning to converge. This is a challenge in the field of medicine…
Bridging Dimensions: Confident Reachability for High-Dimensional Controllers
Yuang Geng, Jake Brandon Baldauf, Souradeep Dutta +2
Autonomous systems are increasingly implemented using end-to-end learning-based controllers. Such controllers make decisions that are executed on the real system, with images as on…
Memory-Consistent Neural Networks for Imitation Learning
Kaustubh Sridhar, Souradeep Dutta, Dinesh Jayaraman +2
Imitation learning considerably simplifies policy synthesis compared to alternative approaches by exploiting access to expert demonstrations. For such imitation policies, errors aw…
Distributionally Robust Statistical Verification with Imprecise Neural Networks
Souradeep Dutta, Michele Caprio, Vivian Lin +5
A particularly challenging problem in AI safety is providing guarantees on the behavior of high-dimensional autonomous systems. Verification approaches centered around reachability…
Using Semantic Information for Defining and Detecting OOD Inputs
Ramneet Kaur, Xiayan Ji, Souradeep Dutta +5
As machine learning models continue to achieve impressive performance across different tasks, the importance of effective anomaly detection for such models has increased as well. I…
DC4L: Distribution Shift Recovery via Data-Driven Control for Deep Learning Models
Vivian Lin, Kuk Jin Jang, Souradeep Dutta +3
Deep neural networks have repeatedly been shown to be non-robust to the uncertainties of the real world, even to naturally occurring ones. A vast majority of current approaches hav…
Credal Bayesian Deep Learning
Michele Caprio, Souradeep Dutta, Kuk Jin Jang +4
Uncertainty quantification and robustness to distribution shifts are important goals in machine learning and artificial intelligence. Although Bayesian Neural Networks (BNNs) allow…
Guaranteed Conformance of Neurosymbolic Models to Natural Constraints
Kaustubh Sridhar, Souradeep Dutta, James Weimer +1
Deep neural networks have emerged as the workhorse for a large section of robotics and control applications, especially as models for dynamical systems. Such data-driven models are…
Towards Alternative Techniques for Improving Adversarial Robustness: Analysis of Adversarial Training at a Spectrum of Perturbations
Kaustubh Sridhar, Souradeep Dutta, Ramneet Kaur +3
Adversarial training (AT) and its variants have spearheaded progress in improving neural network robustness to adversarial perturbations and common corruptions in the last few year…
Memory Classifiers: Two-stage Classification for Robustness in Machine Learning
Souradeep Dutta, Yahan Yang, Elena Bernardis +2
The performance of machine learning models can significantly degrade under distribution shifts of the data. We propose a new method for classification which can improve robustness…
Exploring with Sticky Mittens: Reinforcement Learning with Expert Interventions via Option Templates
Souradeep Dutta, Kaustubh Sridhar, Osbert Bastani +4
Long horizon robot learning tasks with sparse rewards pose a significant challenge for current reinforcement learning algorithms. A key feature enabling humans to learn challenging…
Output Range Analysis for Deep Neural Networks
Souradeep Dutta, Susmit Jha, Sriram Sanakaranarayanan +1
Deep neural networks (NN) are extensively used for machine learning tasks such as image classification, perception and control of autonomous systems. Increasingly, these deep NNs a…