Autodeuq: Automated deep ensemble with uncertainty quantification
Deep neural networks are powerful predictors for a variety of tasks. However, they do not
capture uncertainty directly. Using neural network ensembles to quantify uncertainty is …
capture uncertainty directly. Using neural network ensembles to quantify uncertainty is …
SOPA‐GA‐CNN: Synchronous optimisation of parameters and architectures by genetic algorithms with convolutional neural network blocks for securing Industrial …
In recent years, deep learning has been applied to a variety of scenarios in Industrial
Internet of Things (IIoT), including enhancing the security of IIoT. However, the existing deep …
Internet of Things (IIoT), including enhancing the security of IIoT. However, the existing deep …
Database native model selection: Harnessing deep neural networks in database systems
The growing demand for advanced analytics beyond statistical aggregation calls for
database systems that support effective model selection of deep neural networks (DNNs) …
database systems that support effective model selection of deep neural networks (DNNs) …
Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles
Classical problems in computational physics such as data-driven forecasting and signal
reconstruction from sparse sensors have recently seen an explosion in deep neural network …
reconstruction from sparse sensors have recently seen an explosion in deep neural network …
Evostore: Towards scalable storage of evolving learning models
Deep Learning (DL) has seen rapid adoption in all domains. Since training DL models is
expensive, both in terms of time and resources, application workflows that make use of DL …
expensive, both in terms of time and resources, application workflows that make use of DL …
[HTML][HTML] Spatially local surrogate modeling of subgrid-scale effects in idealized atmospheric flows: a deep learned approach using high-resolution simulation data
M Gopalakrishnan Meena, MR Norman… - … Intelligence for the …, 2024 - journals.ametsoc.org
We introduce a machine learned surrogate model from high-resolution simulation data to
capture the subgrid-scale effects in dry, stratified atmospheric flows. We use deep neural …
capture the subgrid-scale effects in dry, stratified atmospheric flows. We use deep neural …
TabNAS: Rejection sampling for neural architecture search on tabular datasets
The best neural architecture for a given machine learning problem depends on many
factors: not only the complexity and structure of the dataset, but also on resource constraints …
factors: not only the complexity and structure of the dataset, but also on resource constraints …
Anytime neural architecture search on tabular data
The increasing demand for tabular data analysis calls for transitioning from manual
architecture design to Neural Architecture Search (NAS). This transition demands an …
architecture design to Neural Architecture Search (NAS). This transition demands an …
Accuracy-constrained efficiency optimization and GPU profiling of CNN inference for detecting drainage crossing locations
The accurate and efficient determination of hydrologic connectivity has garnered significant
attention from both academic and industrial sectors due to its critical implications for …
attention from both academic and industrial sectors due to its critical implications for …
Unraveling the Correlation between the Interface Structures and Tunable Magnetic Properties of La1–xSrxCoO3−δ/La1–xSrxMnO3−δ Bilayers Using Deep …
Perovskite oxides are gaining significant attention for use in next-generation magnetic and
ferroelectric devices due to their exceptional charge transport properties and the opportunity …
ferroelectric devices due to their exceptional charge transport properties and the opportunity …