Results 11 to 20 of about 71,961 (266)
Generative modeling of turbulence
Physics of Fluids, 2022 We present a mathematically well-founded approach for the synthetic modeling of turbulent flows using generative adversarial networks (GAN). Based on the analysis of chaotic, deterministic systems in terms of ergodicity, we outline a mathematical proof that GAN can actually learn to sample state snapshots from the invariant measure of the chaotic ...
C. Drygala +3 more
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The generic model of general relativity [PDF]
Arabian Journal of Mathematics, 2019 55 pages, no ...
Tsamparlis, M., Paliathanasis, A.
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Review of Generative Reinforcement Learning Based on Sequence Modeling [PDF]
Jisuanji kexueReinforcement learning is a branch of machine learning on how to learn decisions,which is a sequential decision-making problem that involves repeatedly interacting with the environment to find the optimal strategy through trial and error.Reinforcement ...
YAO Tianlei, CHEN Xiliang, YU Peiyi
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Predicting cellular responses to complex perturbations in high‐throughput screens
Molecular Systems Biology, 2023 Recent advances in multiplexed single‐cell transcriptomics experiments facilitate the high‐throughput study of drug and genetic perturbations. However, an exhaustive exploration of the combinatorial perturbation space is experimentally unfeasible ...
Mohammad Lotfollahi +18 more
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Decontextualized learning for interpretable hierarchical representations of visual patterns
Patterns, 2021 Summary: Apart from discriminative modeling, the application of deep convolutional neural networks to basic research utilizing natural imaging data faces unique hurdles.
Robert Ian Etheredge +2 more
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Learning With Imbalanced Data in Smart Manufacturing: A Comparative Analysis
IEEE Access, 2021 The Internet of Things (IoT) paradigm is revolutionising the world of manufacturing into what is known as Smart Manufacturing or Industry 4.0. The main pillar in smart manufacturing looks at harnessing IoT data and leveraging machine learning (ML) to ...
Yasmin Fathy +2 more
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Kompren: modeling and generating model slicers [PDF]
Software & Systems Modeling, 2012 Among model comprehension tools, model slicers are tools that extract a subset of model elements, for a specific purpose. Model slicers provide a mechanism to isolate and focus on parts of the model, thereby improving the overall analysis process. However, existing slicers are dedicated to a specific modeling language.
Blouin, Arnaud +3 more
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A Generic Model of Consciousness
Journal of Artificial Intelligence and Consciousness, 2023 This is a model of consciousness. The hard problem of consciousness, what it feels like, is answered. The work builds on medical research analyzing the source and mechanisms associated with our feelings. It goes further by describing a generic model with wide applicability.
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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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