15 citations · 21 across the 8 of their papers we have counts for
8 papers
Ensembles of Neural Surrogates for Parametric Sensitivity in Ocean Modeling
Yixuan Sun, Romain Egele, Sri Hari Krishna Narayanan +6
Accurate simulations of the oceans are crucial in understanding the Earth system. Despite their efficiency, simulations at lower resolutions must rely on various uncertain paramete…
REMEDI: Corrective Transformations for Improved Neural Entropy Estimation
Viktor Nilsson, Anirban Samaddar, Sandeep Madireddy +1
Information theoretic quantities play a central role in machine learning. The recent surge in the complexity of data and models has increased the demand for accurate estimation of…
Improving Performance in Continual Learning Tasks using Bio-Inspired Architectures
Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash
The ability to learn continuously from an incoming data stream without catastrophic forgetting is critical to designing intelligent systems. Many approaches to continual learning r…
Unified Probabilistic Neural Architecture and Weight Ensembling Improves Model Robustness
Sumegha Premchandar, Sandeep Madireddy, Sanket Jantre +1
Robust machine learning models with accurately calibrated uncertainties are crucial for safety-critical applications. Probabilistic machine learning and especially the Bayesian for…
HPC Storage Service Autotuning Using Variational-Autoencoder-Guided Asynchronous Bayesian Optimization
Matthieu Dorier, Romain Egele, Prasanna Balaprakash +5
Distributed data storage services tailored to specific applications have grown popular in the high-performance computing (HPC) community as a way to address I/O and storage challen…
Sequential Bayesian Neural Subnetwork Ensembles
Sanket Jantre, Shrijita Bhattacharya, Nathan M. Urban +4
Deep ensembles have emerged as a powerful technique for improving predictive performance and enhancing model robustness across various applications by leveraging model diversity. H…
A Taxonomy of Error Sources in HPC I/O Machine Learning Models
Mihailo Isakov, Mikaela Currier, Eliakin del Rosario +6
I/O efficiency is crucial to productivity in scientific computing, but the increasing complexity of the system and the applications makes it difficult for practitioners to understa…
Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck
Anirban Samaddar, Sandeep Madireddy, Prasanna Balaprakash +3
The information bottleneck framework provides a systematic approach to learning representations that compress nuisance information in the input and extract semantically meaningful…