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A set of networks that satisfies given structural characteristics can be treated as a network ensemble.[1] Brought up by Ginestra Bianconi in 2007, the entropy of a network ensemble measures the level of the order or uncertainty of a network ensemble.[2]
The entropy is the logarithm of the number of graphs.[3] Entropy can also be defined in one network. Basin entropy is the logarithm of the attractors in one Boolean network.[4]
Employing approaches from statistical mechanics, the complexity, uncertainty, and randomness of networks can be described by network ensembles with different types of constraints.[5]
^Levin, E.; Tishby, N.; Solla, S.A. (October 1990). "A statistical approach to learning and generalization in layered neural networks". Proceedings of the IEEE. 78 (10): 1568–1574. doi:10.1109/5.58339. ISSN 1558-2256. S2CID 5254307.
the entropyof a networkensemble measures the level of the order or uncertainty of a networkensemble. The entropy is the logarithm of the number of graphs...
In network science, the networkentropy is a disorder measure derived from information theory to describe the level of randomness and the amount of information...
difficult to find a good one. Ensembles combine multiple hypotheses to form a (hopefully) better hypothesis. Ensemble learning trains two or more Machine...
Entropy is a scientific concept that is most commonly associated with a state of disorder, randomness, or uncertainty. The term and the concept are used...
of the microstate i taken from an equilibrium ensemble, and k B {\displaystyle k_{B}} is the Boltzmann constant. The defining expression for entropy in...
entropyof X. The above definition of transfer entropy has been extended by other types ofentropy measures such as Rényi entropy. Transfer entropy is...
The principle of maximum entropy states that the probability distribution which best represents the current state of knowledge about a system is the one...
in a cyclic process." The second law of thermodynamics establishes the concept ofentropy as a physical property of a thermodynamic system. It predicts...
One special class ofensemble is those ensembles that do not evolve over time. These ensembles are known as equilibrium ensembles and their condition...
particular underlying force on the atomic scale. In the canonical ensemble, the entropic force F {\displaystyle \mathbf {F} } associated to a macrostate...
is a probability distribution over an ensemble, this leads naturally to the definition of the von Neumann entropy: S ( ρ ) = − Tr ( ρ log 2 ρ ) . {\displaystyle...
molecular network. As a rubber band is stretched, some of the network chains are forced to become straight and this causes a decrease in their entropy. It is...
canonical ensemble and isothermal–isobaric ensemble. The generalized Boltzmann distribution is usually derived from the principle of maximum entropy, but there...
observing the other random variable. The concept of mutual information is intimately linked to that ofentropyof a random variable, a fundamental notion in...
process of transferring knowledge from a large model to a smaller one. While large models (such as very deep neural networks or ensemblesof many models)...
usual Boltzmann-Gibbs or Shannon entropy. In this sense, the Gini impurity is nothing but a variation of the usual entropy measure for decision trees. Used...
Maximal entropy random walk (MERW) is a popular type of biased random walk on a graph, in which transition probabilities are chosen accordingly to the...
A convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns features by itself via filter (or kernel) optimization...
computational methods. Deep neural networks can be used to estimate the entropyof a stochastic process and called Neural Joint Entropy Estimator (NJEE). Such an...
triggered a higher entropy in the EEG signal and a higher level of functional connectivity between brain areas, both well-known biomarkers of actual psychedelic...
Quantum information refers to both the technical definition in terms of Von Neumann entropy and the general computational term. It is an interdisciplinary field...
A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative AI. The concept...
of a neural network-based classifier. Such networks are commonly trained under a log loss (or cross-entropy) regime, giving a non-linear variant of multinomial...
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