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Found 8 papers, 3 papers with code
AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems
Although Federated Learning (FL) is promising to enable collaborative learning among Artificial Intelligence of Things (AIoT) devices, it suffers from the problem of low classification performance due to various heterogeneity factors (e. g., computing capacity, memory size) of devices and uncertain operating environments.
Fedward: Flexible Federated Backdoor Defense Framework with Non-IID Data
This ensures that Fedward can maintain the performance for the Non-IID scenario.
Language Models are Few-shot Learners for Prognostic Prediction
Clinical prediction is an essential task in the healthcare industry.
Masked Image Modeling Advances 3D Medical Image Analysis
Recently, masked image modeling (MIM) has gained considerable attention due to its capacity to learn from vast amounts of unlabeled data and has been demonstrated to be effective on a wide variety of vision tasks involving natural images.
DCAP: Deep Cross Attentional Product Network for User Response Prediction
Prior studies in predicting user response leveraged the feature interactions by enhancing feature vectors with products of features to model second-order or high-order cross features, either explicitly or implicitly.
ASM2TV: An Adaptive Semi-Supervised Multi-Task Multi-View Learning Framework for Human Activity Recognition
On the one hand, multiple views across tasks possibly relate to each other under practical situations.
Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoT
This paper presented GTA, a new framework for multivariate time series anomaly detection that involves automatically learning a graph structure, graph convolution, and modeling temporal dependency using a Transformer-based architecture.
Multi-Task Time Series Forecasting With Shared Attention
Time series forecasting is a key component in many industrial and business decision processes and recurrent neural network (RNN) based models have achieved impressive progress on various time series forecasting tasks.
