7 hours ago · In Parametrized classifiers for optimal EFT sensitivity [11] , the authors develop a neural-network-based approach for learning optimal SMEFT classifiers up to ...
24 hours ago · This work investigates the effectiveness of deep neural networks within the realm of battery charging. This is done by introducing an innovative control ...
23 hours ago · In this paper, we explore the application of Physics-Informed Neural Networks (PINNs) in parameter identification for continuum models of manufacturing ...
23 hours ago · This paper proposes a novel framework for characterising mixed traffic conditions based on vehicle class-wise speeds rather than a single value of the ...
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7 hours ago · Yann LeCun's Deep Learning Course covers the latest techniques in both deep learning and representation learning, focusing on supervised/self-supervised ...
7 hours ago · Finally, we demonstrate applications of our framework to well-studied problems including performative prediction, recommendations for adaptive agents, adaptive ...
24 hours ago · This paper proposes to design a joint graph data and architecture mechanism, which identifies important sub-architectures via the valuable graph data. To search ...
4 hours ago · Firstly, a balancing strategy was developed to tackle sample imbalance by assigning and optimizing weights for samples in imbalanced categories. Graph theory ...
20 hours ago · To address the issue of the lack of specialized data filtering algorithms for dataset production, we proposed an image filtering algorithm.
53 minutes ago · One of current issues is finding out the method of implementing the training and prediction of decision tree models in a FL environment. This survey addresses ...