The cross-entropy (CE) method is a new generic approach to combinatorial and multi-extremal optimization and rare event simulation. The purpose of this tutorial is to give a gentle introduction to the CE method.
R. Y. Rubinstein and D. P. Kroese, A Tutorial Introduction to the Cross- Entropy Method. Springer New York, 2004.Kolodner, J., Leake, D. A tutorial introduction to case-based reasoning. Case-based reasoning : experiences, lessons, & future directions. San Francisco: AAAI Press, 1996....
A tutorial on the cross-entropy method 2005, Annals of Operations Research View all citing articles on ScopusWalter J. Gutjahr received his M.Sc. and Ph.D. degrees in mathematics from the University of Vienna, Austria in 1980 and 1985, respectively. From 1980 to 1988 he was with Siemens ...
Network science investigates methodologies that summarise relational data to obtain better interpretability. Identifying modular structures is a fundamental task, and assessment of the coarse-grain level is its crucial step. Here, we propose principled,
Policy (π): Thepolicyis the strategy that the agent employs to determine the next action based on the current state. It maps states to actions, the actions that promise the highest reward. Value (V): The expected long-term return with discount, as opposed to the short-term rewardR.Vπ...
Optimize Weights:UseGradient Descentto reduce the cost function. 2. Cost Function in Logistic Regression Logistic regression uses the log-loss (cross-entropy loss) function to measure error: Types of Logistic Regression 1. Binary Logistic Regression ...
We finally demonstrate the potential ability of our model to explain functional implications of putative disease-associated genetic variants and discriminate disease-related enhancers. The source code and detailed tutorial of DeepCAPE are freely available at https://github.com/ShengquanChen/DeepCAPE....
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Dissecting the cellular heterogeneity embedded in single-cell transcriptomic data is challenging. Although many methods and approaches exist, identifying cell states and their underlying topology is still a major challenge. Here, we introduce the concept
convolutional neural network consisting of two convolutional layers (each followed by max-pooling) and a fully connected layer. This architecture is derived from theMNIST tensorflow tutorial. The network was trained against an iterative adversary that is allowed to perturb each pixel by at mostepsilon...