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Found 8 papers, 4 papers with code
On the Computational Complexity of Private High-dimensional Model Selection
We consider the problem of model selection in a high-dimensional sparse linear regression model under privacy constraints.
FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model Interpolation
In this paper, we propose a novel federated model soup method (i. e., selective interpolation of model parameters) to optimize the trade-off between local and global performance.
Low-code LLM: Graphical User Interface over Large Language Models
By introducing this framework, we aim to bridge the gap between humans and LLMs, enabling more effective and efficient utilization of LLMs for complex tasks.
Cervical Glandular Cell Detection from Whole Slide Image with Out-Of-Distribution Data
Cervical glandular cell (GC) detection is a key step in computer-aided diagnosis for cervical adenocarcinomas screening.
Blockchain-empowered Data-driven Networks: A Survey and Outlook
The paths leading to future networks are pointing towards a data-driven paradigm to better cater to the explosive growth of mobile services as well as the increasing heterogeneity of mobile devices, many of which generate and consume large volumes and variety of data.
Networking and Internet Architecture
Frontoparietal Connectivity Neurofeedback Training for Promotion of Working Memory: An fNIRS Study in Healthy Male Participants
Neurofeedback cognitive training is a promising tool used to promote cognitive functions effectively and efficiently.
Dynamic Point Cloud Denoising via Manifold-to-Manifold Distance
In particular, we define a manifold-to-manifold distance and its discrete counterpart on graphs to measure the variation-based intrinsic distance between surface patches in the temporal domain, provided that graph operators are discrete counterparts of functionals on Riemannian manifolds.
3D Dynamic Point Cloud Denoising via Spatial-Temporal Graph Learning
Finally, based on the spatial-temporal graph learning, we formulate dynamic point cloud denoising as the joint optimization of the desired point cloud and underlying spatio-temporal graph, which leverages both intra-frame affinities and inter-frame consistency and is solved via alternating minimization.
