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School of Computational Science and Engineering
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Methodologies for co-designing supercomputer-scale systems and deep learning software
Scalable Data Mining via Constrained Low Rank Approximation
Automated surface finish inspection using convolutional neural networks
Voxel-based offsetting at high resolution with tunable speed and precision using hybrid dynamic trees
Fast and compact neural network via Tensor-Train reparameterization
Performance Primitives for Artificial Neural Networks
Scalable tensor decompositions in high performance computing environments
Multifidelity Memory System Simulation
Diagnosing performance bottlenecks in HPC applications
Scalable and resilient sparse linear solvers