搜索结果: 1-15 共查到“数学 Networks”相关记录148条 . 查询时间(0.125 秒)
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Entropy-Dissipation Informed Neural Networks for McKean-Vlasov type PDEs
McKean-Vlasov型 偏微分方程 熵耗散 信息神经网络
2023/4/13
We extend the concept of self-consistency for the Fokker-Planck equation (FPE) [Shen et al., 2022] to the more general McKean-Vlasov equation (MVE). While FPE describes the macroscopic behavior of par...
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Estimating Time-Varying Networks for High-Dimensional Time Series
高维 时间序列 时变网络
2023/4/25
We explore time-varying networks for high-dimensional locally stationary time series, using the large VAR model framework with both the transition and (error) precision matrices evolving smoothly over...
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Convergence Thery of Deep Neural Networks: Arbitrary Activation Functions and Pooling
深度神经网络 激活函数 池化
2023/4/28
Deep neural networks, as a powerful system to represent high dimensional complex functions, play a key role in deep learning. Convergence of deep neural networks is a fundamental issue in building the...
三亚国际数学论坛：图与网络对称性（International Workshop on Symmetries of Graph and Networks）
三亚国际数学论坛 图与网络对称性
2018/1/11
Nowadays, graph symmetries are becoming a very important and rapidly growing area of study, and often looked at closely by computer scientists and other network designers. One significant instance of ...
Nowadays, graph symmetries are becoming a very important and rapidly growing area of study, and often looked at closely by computer scientists and other network designers. One significant instance of ...
2018年复杂网络高维数据统计挑战研讨会（Meeting the Statistical Challenges in High Dimensional Data and Complex Networks）
2018年 复杂网络高维数据统计挑战 研讨会
2017/11/24
The program aims at showing the role of modern statistical methods in complex data and serves to support interactions among mathematicians, statisticians, engineers and scientists working in the inter...
MAP SOURCE SEPARATION USING BELIEF PROPAGATION NETWORKS
MAP SOURCE SEPARATION BELIEF PROPAGATION NETWORKS
2015/9/29
In this paper we continue our treatment of source separation based on dynamic sparse source signal models. Source signals are modeled in frequency domain as a product of a Bernoulli selection variable...
Energy-Efficient Resource Allocation in Wireless Networks with Quality-of-Service Constraints
Energy efficiency delay quality of service game theory Nash equilibrium power and rate control admission control cross-layer design
2015/9/29
A game-theoretic model is proposed to study the cross-layer problem of joint power and rate control with quality of service (QoS) constraints in multiple-access networks. In the proposed game, each us...
Abelian networks are systems of communicating automata satisfying a local commutativity condition. We show that a finite irreducible abelian network halts on all inputs if and only if all eigenvalues ...
The critical group of an abelian network is a finite abelian group that governs the behavior of the network on large inputs. It generalizes the sandpile group of a graph. We show that the critical gro...
In Dhar’s model of abelian distributed processors, finite automata occupy the vertices of a graph and communicate via the edges. A local commutativity condition ensures that the final output does not ...
Chaos and Robustness in a Single Family of Genetic Oscillatory Networks
Chaos and Robustness Genetic Oscillatory Networks
2014/12/12
Genetic oscillatory networks can be mathematically modeled with delay differential equations (DDEs). Interpreting genetic networks with DDEs gives a more intuitive understanding from a biological stan...
Scoring Bayesian Networks with Informative, Causal and Associative Priors
Scoring Bayesian Networks Informative Causal Associative Priors
2012/11/26
A significant theoretical advantage of search-and-score methods for learning Bayesian Networks is that they can accept informative prior beliefs for each possible network, thus complementing the data....
Learning Linear Bayesian Networks with Latent Variables
Linear Networks Bayesian Latent Variables
2012/11/23
This work considers the problem of learning linear Bayesian networks when some of the variables are unobserved. Identifiability and efficient recovery from low-order observable moments are established...
Hierarchical control based output synchronization of coexisting attractor networks
Hierarchical control passive control composite Lyapunov function theNewton-Leipnik equation attractor
2012/8/10
This paper introduces the concept of hierarchical control based output
synchronization of coexisting attractor networks. Under the new framework, each
dynamical node is made passive through intra co...