To overcome such a limitation, we propose a Machine Learning Guided Optimization methodology to build a new objective function based on simulations and historical data. This way, we are able to take the demand's
ML-guided local reaction optimization, or self-optimization, is an automated and generalizable approach that can accelerate the discovery of optimal reaction conditions, as illustrated in Figure 3. The first step is problem formulation, which involves defining the reaction parameters to be optimized and...
AI, machine learning (ML), natural language processing (NLP), deep learning (DL), and others enable healthcare stakeholders and medical professionals to identify healthcare needs and solutions faster and with more accuracy. Does Your Business Really Need An Enterprise Artificial Intelligence AI vs. ...
Yinheng Zhu et al. present AutoCAR, an automated algorithm for reconstructing three-dimensional cardiovascular structures from X-ray images. It uses transfer learning and vascular graph optimization to achieve high efficiency and accuracy, with the goal to enable medical procedures and diagnosis in rea...
Cole: compiler optimization level exploration - Kenneth Hoste and Lieven Eeckhout. CGO 2008. MILEPOST GCC: machine learning based research compiler - Grigori Fursin, Cupertino Miranda, Olivier Temam, Mircea Namolaru, Elad Yom-Tov, Ayal Zaks, Bilha Mendelson et al., 2008 Evaluating heuristic optimi...
however generating scientifically meaningful laws and determining their consistency remains challenging. The authors introduce an approach that exploits both experimental data and underlying theory in symbolic form to generate formulas that hold scientific significance by solving polynomial optimization problems. ...
Chen, X., Tian, Y.: Learning to perform local rewriting for combinatorial optimization. In: International Conference on Neural Information Processing Systems (NeurIPS) (2019) Google Scholar Cheng, L., Wong, M.D.: Floorplan design for multimillion gate FPGAs. IEEE Trans. Comput. Aided Design...
Finally, we leveraged an ANE compiler optimization that splits the computation of layers with large spatial dimensions into small spatial tiles, and makes a trade-off between latency and memory usage. Together, these techniques yielded an extreme reduction in the memory footprint of our model and...
Theano: a CPU and GPU math compiler in Python. In: Proc. 9th Python in Science Conf. 2010;1:3–10. Abadi, M., et al., Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467, 2016. Kingma, D. and J. Ba, Adam: A method for ...
Optimization Semi-Supervised,-Weakly-Supervised-and-Unsupervised-Learning Transfer Learning Trustworthy Machine Learning To reduce class imbalance, we separate some of the hot sub-topics from the original categorization of ACL and ICML submissions. E.g., Named Entity Recognition is a first-level area ...