Results 51 to 60 of about 24,500,069 (341)
Journal of Advances in Modeling Earth Systems, 2023 Subgrid parameterizations of mesoscale eddies continue to be in demand for climate simulations. These subgrid parameterizations can be powerfully designed using physics and/or data‐driven methods, with uncertainty quantification.
Pavel Perezhogin +2 more
doaj +1 more source
De Novo Design with Deep Generative Models Based on 3D Similarity Scoring
, 2021 We have demonstrated the utility of a 3D shape and pharmacophore similarity scoring component in molecular design with a deep generative model trained with reinforcement learning.
Kostas, Papadopoulos +4 more
core +1 more source
A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys) [PDF]
Knowledge Discovery and Data MiningTraditional recommender systems typically use user-item rating histories as their main data source. However, deep generative models now have the capability to model and sample from complex data distributions, including user-item interactions, text ...
Yashar Deldjoo +9 more
semanticscholar +1 more source
Survey on Generative Diffusion Model [PDF]
Jisuanji kexueDiffusion models have shown high-quality sample generation ability in the field of generative models,and constantly set new records for image generation evaluation indicator FID scores since their introduction,and has become a research hotspot in this ...
YAN Zhihao, ZHOU Zhangbing, LI Xiaocui
doaj +1 more source
Exact Rank Reduction of Network Models
Physical Review X, 2019 With the advent of the big data era, generative models of complex networks are becoming elusive from direct computational simulation. We present an exact, linear-algebraic reduction scheme of generative models of networks.
Eugenio Valdano, Alex Arenas
doaj +1 more source
On the Generalization of Diffusion Model
CoRR, 2023 The diffusion probabilistic generative models are widely used to generate high-quality data. Though they can synthetic data that does not exist in the training set, the rationale behind such generalization is still unexplored. In this paper, we formally define the generalization of the generative model, which is measured by the mutual information ...
Mingyang Yi, Jiacheng Sun, Zhenguo Li
openaire +2 more sources
International Journal of Interactive Multimedia and Artificial Intelligence, 2023 Artificial Intelligence has become a focal point of interest across various sectors due to its ability to generate creative and realistic outputs. A specific subset, generative artificial intelligence, has seen significant growth, particularly in late ...
Francisco José García Peñalvo +1 more
doaj +1 more source
Automatic model generation [PDF]
Proceedings of the 18th conference on Winter simulation - WSC '86, 1986 The Facility Analysis and Simulation Technique (FAST) is a product which has been developed by the Electronics Automation Application Center (EAAC) of the General Electric Company. Currently in version 2.0, FAST has been developed to analyze electronics facilities resource requirements by utilizing years of EAAC expertise in this environment.
openaire +1 more source
On Generalizations of the Engset Model [PDF]
IEEE Communications Letters, 2007 The Engset model has been extensively studied and widely used for blocking probability evaluation in telecommunications networks. In 1957, J.W. Cohen considered two generalizations of the Engset model: 1) permitting the distributions of the holding time and interarrival time to differ from source to source; 2) permitting the idle time distribution to ...
Eric W. M. Wong +2 more
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Learning generative texture models with extended Fields-of-Experts [PDF]
, 2009 We evaluate the ability of the popular Field-of-Experts (FoE) to model structure in images. As a test case we focus on modeling synthetic and natural textures.
Geoffrey E. Hinton +5 more
core +1 more source