Zobrazeno 1 - 10
of 391
pro vyhledávání: '"Zhang Zezhong"'
We propose nonuniform data-driven parameter distributions for neural network initialization based on derivative data of the function to be approximated. These parameter distributions are developed in the context of non-parametric regression models ba
Externí odkaz:
http://arxiv.org/abs/2410.02132
By working out the Bethe sum rule, a boundary condition that takes the form of a linear equality is derived for the fine structure observed in ionization edges present in electron energy-loss spectra. This condition is subsequently used as a constrai
Externí odkaz:
http://arxiv.org/abs/2408.11870
Autor:
Zhang, Zezhong, Lobato, Ivan, Brown, Hamish, Lamoen, Dirk, Jannis, Daen, Verbeeck, Johan, Van Aert, Sandra, Nellist, Peter D.
The rich information of electron energy-loss spectroscopy (EELS) comes from the complex inelastic scattering process whereby fast electrons transfer energy and momentum to atoms, exciting bound electrons from their ground states to higher unoccupied
Externí odkaz:
http://arxiv.org/abs/2405.10151
In the 6G era, real-time radio resource monitoring and management are urged to support diverse wireless-empowered applications. This calls for fast and accurate estimation on the distribution of the radio resources, which is usually represented by th
Externí odkaz:
http://arxiv.org/abs/2402.02729
The impressive expressive power of deep neural networks (DNNs) underlies their widespread applicability. However, while the theoretical capacity of deep architectures is high, the practical expressive power achieved through successful training often
Externí odkaz:
http://arxiv.org/abs/2312.12578
This paper tackles the intricate task of jointly estimating state and parameters in data assimilation for stochastic dynamical systems that are affected by noise and observed only partially. While the concept of ``optimal filtering'' serves as the cu
Externí odkaz:
http://arxiv.org/abs/2312.10503
Semantic communication is widely touted as a key technology for propelling the sixth-generation (6G) wireless networks. However, providing effective semantic representation is quite challenging in practice. To address this issue, this article takes a
Externí odkaz:
http://arxiv.org/abs/2311.12443
We present a supervised learning framework of training generative models for density estimation. Generative models, including generative adversarial networks, normalizing flows, variational auto-encoders, are usually considered as unsupervised learni
Externí odkaz:
http://arxiv.org/abs/2310.14458
We propose an ensemble score filter (EnSF) for solving high-dimensional nonlinear filtering problems with superior accuracy. A major drawback of existing filtering methods, e.g., particle filters or ensemble Kalman filters, is the low accuracy in han
Externí odkaz:
http://arxiv.org/abs/2309.00983
We introduce a score-based generative sampling method for solving the nonlinear filtering problem with robust accuracy. A major drawback of existing nonlinear filtering methods, e.g., particle filters, is the low stability. To overcome this issue, we
Externí odkaz:
http://arxiv.org/abs/2306.09282