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Found 6 papers, 1 papers with code
PointCutMix: Regularization Strategy for Point Cloud Classification
As 3D point cloud analysis has received increasing attention, the insufficient scale of point cloud datasets and the weak generalization ability of networks become prominent.
Ranked #3 on
3D Point Cloud Classification
on ModelNet40-C
Federated Multi-task Hierarchical Attention Model for Sensor Analytics
The attention mechanism of the proposed model seeks to extract feature representations from the input and learn a shared representation focused on time dimensions across multiple sensors.
Asynchronous Online Federated Learning for Edge Devices with Non-IID Data
In this paper, we present an Asynchronous Online Federated Learning (ASO-Fed) framework, where the edge devices perform online learning with continuous streaming local data and a central server aggregates model parameters from clients.
FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers
By bridging the synchronous and asynchronous training through tiering, FedAT minimizes the straggler effect with improved convergence speed and test accuracy.
Asynchronous Federated Learning for Sensor Data with Concept Drift
We propose a novel approach, FedConD, to detect and deal with the concept drift on local devices and minimize the effect on the performance of models in asynchronous FL.
Rich Semantic Knowledge Enhanced Large Language Models for Few-shot Chinese Spell Checking
We found that by introducing a small number of specific Chinese rich semantic structures, LLMs achieve better performance than the BERT-based model on few-shot CSC task.
