operator splittingAnalyzing the worst-case performance of deep neural networks against input perturbations amounts to solving a large-scale non-convex optimization problem, for which several past works have proposed convex relaxations as a promising alternative. However, even for reasonably-sized neural ...
Full size image Beyond integrating metacells themselves, we further explored recovering the integrated embedding of original cells using metacell integration results. Specifically, we trained a simple neural network to map from raw data space to Harmony-integrated space, leveraging the original and integr...
Since the neural network helps model a non-linear class of functions, the approach helped in compression by mapping the image pixel values to quantized codes and then decode them again back to pixels. The challenge was to design a multi-scale model where lower resolution encoding could help to...
Fourier Neural Operator for Parametric Partial Differential Equations arXiv [cs.LG]. arXiv http://arxiv.org/abs/2010.08895 (2020) Google Scholar Li et al., 2021 X. Li, J. Xiao, J.B. Fisher, D.D. Baldocchi ECOSTRESS estimates gross primary production with fine spatial resolution for diffe...
This chapter presents the authors’ work for the Case Study entitled “Delivering Social Media with Scalability” within the framework of High-Performance Modelling and Simulation for Big Data Applications (cHiPSet) COST Action 1406. We identify some core research areas and give an outline of the ...
mean field game; scalable learning; physics-informed neural operator MSC: 91A13; 91A25; 91A801. Introduction In contrast to numerical solvers for partial differential equations (PDEs), recent years have seen a growing trend of using neural networks (NNs) to approximate PDE solutions because of ...
In Algorithm 1, we first initialize the neural operator 𝒢𝜃Gθ, parameterized by 𝜃θ. During the ith iteration of the training process, we first sample a batch of initial population densities 𝝆0ρ0 and terminal costs 𝑽𝑇VT. We use FNO to generate the population density and val...
Numerical resolution of McKean-Vlasov FBSDEs using neural networks. Methodol. Comput. Appl. Probab. 2022, 24, 2557–2586. [Google Scholar] [CrossRef] Chen, X.; Fu, Y.; Liu, S.; Di, X. Physics-Informed Neural Operator for Coupled Forward-Backward Partial Differential Equations. In ...
Numerical resolution of McKean-Vlasov FBSDEs using neural networks. Methodol. Comput. Appl. Probab. 2022, 24, 2557–2586. [Google Scholar] [CrossRef] Chen, X.; Fu, Y.; Liu, S.; Di, X. Physics-Informed Neural Operator for Coupled Forward-Backward Partial Differential Equations. In ...
The YOLOv8 deep neural network was used in different sub-architectures to detect people and objects, such as bags, suitcases, etc., in the videos. The weights of the YOLOv8l model with the highest performance were recorded to be used as the input in the DeepSort algorithm. The object an...