To address this problem, we introduce the Model SEED, a web-based resource for high-throughput generation, optimization and analysis of genome-scale metabolic models.

Simplex optimization is one of the simplest algorithms available to train a neural network. Understanding how simplex optimization works, and how it compares to the more commonly used back-propagation ...

Edge inference engines often run a slimmed-down real-time engine that interprets a neural-network model, invoking kernels as it goes. But higher performance can be achieved by pre-compiling the model ...

The Transformer model uses optimization algorithms to adjust its parameters, improving its ability to differentiate between similar-sounding words like "rain" and "reign" in future transcriptions.

Tan Liu, Qinyun Yuan, Lina Wang, Yonggang Wang, Nannan Zhang, Multi-objective optimization for oil-gas production process based on compensation model of comprehensive energy consumption using improved ...

Chen Liang, Sankaran Mahadevan, Multidisciplinary Optimization under Uncertainty Using Bayesian Network, SAE International Journal of Materials and Manufacturing, Vol. 9, No. 2 (May 2016), pp. 419-429 ...

When I asked WAN optimization veterans to weigh in on what they think will be the biggest technology and industry trends to watch during the year ahead, the economy (not surprisingly) played a ...

Network-level optimization is the most critical aspect of the world’s shared energy future. It gives utilities a holistic view of the energy landscape at any given moment, in real time.

This research seeks to develop a predictive model to mitigate potential damages caused by future winter storms. This research utilizes the Light Gradient Boosting Machine (LightGBM), incorporating the ...