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Found 3 papers, 1 papers with code
Serving Deep Learning Model in Relational Databases
Serving deep learning (DL) models on relational data has become a critical requirement across diverse commercial and scientific domains, sparking growing interest recently.
Adaptive Elastic Training for Sparse Deep Learning on Heterogeneous Multi-GPU Servers
We address these challenges with Adaptive SGD, an adaptive elastic model averaging stochastic gradient descent algorithm for heterogeneous multi-GPUs that is characterized by dynamic scheduling, adaptive batch size scaling, and normalized model merging.
Investigating Underlying Drivers of Variability in Residential Energy Usage Patterns with Daily Load Shape Clustering of Smart Meter Data
Large-scale deployment of smart meters has motivated increasing studies to explore disaggregated daily load patterns, which can reveal important heterogeneity across different time scales, weather conditions, as well as within and across individual households.
