82 citations · 231 across the 49 of their papers we have counts for
10 papers · 1 filter
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…
Explainable Graph Pyramid Autoformer for Long-Term Traffic Forecasting
Weiheng Zhong, Tanwi Mallick, Hadi Meidani +2
Accurate traffic forecasting is vital to an intelligent transportation system. Although many deep learning models have achieved state-of-art performance for short-term traffic fore…
Asynchronous Decentralized Bayesian Optimization for Large Scale Hyperparameter Optimization
Romain Egele, Isabelle Guyon, Venkatram Vishwanath +1
Bayesian optimization (BO) is a promising approach for hyperparameter optimization of deep neural networks (DNNs), where each model training can take minutes to hours. In BO, a com…
Multifidelity Reinforcement Learning with Control Variates
Sami Khairy, Prasanna Balaprakash
In many computational science and engineering applications, the output of a system of interest corresponding to a given input can be queried at different levels of fidelity with di…
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…
Deep-Ensemble-Based Uncertainty Quantification in Spatiotemporal Graph Neural Networks for Traffic Forecasting
Tanwi Mallick, Prasanna Balaprakash, Jane Macfarlane
Deep-learning-based data-driven forecasting methods have produced impressive results for traffic forecasting. A major limitation of these methods, however, is that they provide for…
Stabilized Neural Ordinary Differential Equations for Long-Time Forecasting of Dynamical Systems
Alec J. Linot, Joshua W. Burby, Qi Tang +3
In data-driven modeling of spatiotemporal phenomena careful consideration often needs to be made in capturing the dynamics of the high wavenumbers. This problem becomes especially…
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…
Multi-fidelity reinforcement learning framework for shape optimization
Sahil Bhola, Suraj Pawar, Prasanna Balaprakash +1
Deep reinforcement learning (DRL) is a promising outer-loop intelligence paradigm which can deploy problem solving strategies for complex tasks. Consequently, DRL has been utilized…