35 citations · 50 across the 15 of their papers we have counts for
15 papers
Mitigating Task-Order Sensitivity and Forgetting via Hierarchical Second-Order Consolidation
Protik Nag, Krishnan Raghavan, Vignesh Narayanan
We introduce , a framework that couples fast local adaptation with conservative, second-order global consolidat…
Large Language Models for Anomaly Detection in Computational Workflows: from Supervised Fine-Tuning to In-Context Learning
Hongwei Jin, George Papadimitriou, Krishnan Raghavan +5
Anomaly detection in computational workflows is critical for ensuring system reliability and security. However, traditional rule-based methods struggle to detect novel anomalies. T…
Advancing Anomaly Detection in Computational Workflows with Active Learning
Krishnan Raghavan, George Papadimitriou, Hongwei Jin +4
A computational workflow, also known as workflow, consists of tasks that are executed in a certain order to attain a specific computational campaign. Computational workflows are co…
Uncertainty Quantification-Enabled Inversion of Nuclear Euclidean Responses
K. Raghavan, A. Lovato
Nuclear quantum many-body methods rely on integral transform techniques to infer properties of electroweak response functions from ground-state expectation values. Retrieving the e…
Self-supervised Learning for Anomaly Detection in Computational Workflows
Hongwei Jin, Krishnan Raghavan, George Papadimitriou +4
Anomaly detection is the task of identifying abnormal behavior of a system. Anomaly detection in computational workflows is of special interest because of its wide implications in…
Flow-Bench: A Dataset for Computational Workflow Anomaly Detection
George Papadimitriou, Hongwei Jin, Cong Wang +5
A computational workflow, also known as workflow, consists of tasks that must be executed in a specific order to attain a specific goal. Often, in fields such as biology, chemistry…
Learning Continually on a Sequence of Graphs -- The Dynamical System Way
Krishnan Raghavan, Prasanna Balaprakash
Continual learning~(CL) is a field concerned with learning a series of inter-related task with the tasks typically defined in the sense of either regression or classification. In r…
SF-SFD: Stochastic Optimization of Fourier Coefficients to Generate Space-Filling Designs
Manisha Garg, Tyler Chang, Krishnan Raghavan
Due to the curse of dimensionality, it is often prohibitively expensive to generate deterministic space-filling designs. On the other hand, when using na{ï}ve uniform random sampli…
Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles
Romit Maulik, Romain Egele, Krishnan Raghavan +1
Classical problems in computational physics such as data-driven forecasting and signal reconstruction from sparse sensors have recently seen an explosion in deep neural network (DN…
Classification of events from -induced reactions in the MUSIC detector via statistical and ML methods
Krishnan Raghavan, Melina L. Avila, Prasanna Balaprakash +2
The Multi-Sampling Ionization Chamber (MUSIC) detector is typically used to measure nuclear reaction cross sections relevant for nuclear astrophysics, fusion studies, and other app…
Cooperative Deep -learning Framework for Environments Providing Image Feedback
Krishnan Raghavan, Vignesh Narayanan, Jagannathan Sarangapani
In this paper, we address two key challenges in deep reinforcement learning setting, sample inefficiency and slow learning, with a dual NN-driven learning approach. In the proposed…
Learning to Control using Image Feedback
Krishnan Raghavan, Vignesh Narayanan, Jagannathan Saraangapani
Learning to control complex systems using non-traditional feedback, e.g., in the form of snapshot images, is an important task encountered in diverse domains such as robotics, neur…
AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification
Romain Egele, Romit Maulik, Krishnan Raghavan +3
Deep neural networks are powerful predictors for a variety of tasks. However, they do not capture uncertainty directly. Using neural network ensembles to quantify uncertainty is co…
Formalizing the Generalization-Forgetting Trade-off in Continual Learning
Krishnan Raghavan, Prasanna Balaprakash
We formulate the continual learning (CL) problem via dynamic programming and model the trade-off between catastrophic forgetting and generalization as a two-player sequential game.…
Machine learning-based inversion of nuclear responses
Krishnan Raghavan, Prasanna Balaprakash, Alessandro Lovato +2
A microscopic description of the interaction of atomic nuclei with external electroweak probes is required for elucidating aspects of short-range nuclear dynamics and for the corre…