15 citations · 30 across the 4 of their papers we have counts for
4 papers
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…
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…