4 days ago · Traditional physics-based models are first-principled, explainable, and sample-efficient. However, they often rely on strong modeling assumptions and expensive ...
7 days ago · To address this, we introduce SubLIME, a data-efficient evaluation framework that employs adaptive sampling techniques, such as clustering and quality-based ...
Jun 9, 2024 · In this work, we propose an end-to-end adaptive sampling framework based on deep neural networks and the moving mesh method (MMPDE-Net), which can adaptively ...
6 days ago · It dynamically balances the development and exploration phases of evolution by adapting to different search stages.
May 31, 2024 · In this paper we present a novel data-driven subsampling method that can be seamlessly integrated into any neural network architecture to identify the most.
Jun 4, 2024 · We propose an adaptive sampling method for collocation points that is specifically tailored to fluid dynamics and comprehensively compare it with existing ...
Jun 8, 2024 · We study the problem of learning a linear system model from the observations of $M$ clients. The catch: Each client is observing data from a different dynamical ...
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1 day ago · To address this, we introduce SubLIME, a data-efficient evaluation framework that employs adaptive sampling techniques, such as clustering and quality-based ...
Jun 13, 2024 · However, previous MIL methods are barely explore mutual relationship between sampling, feature representation and decision-making. Although DTFD-MaxS gains a ...
6 days ago · Adaptive sampling, which involves increasing the training samples iteratively near the limit state, is a decisive approach widely applied to enhance the ...