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Found 12 papers, 0 papers with code
U-Mixer: An Unet-Mixer Architecture with Stationarity Correction for Time Series Forecasting
Time series forecasting is a crucial task in various domains.
MPR-Net:Multi-Scale Pattern Reproduction Guided Universality Time Series Interpretable Forecasting
Time series forecasting has received wide interest from existing research due to its broad applications and inherent challenging.
Anti-Delay Kalman Filter Fusion Algorithm for Vehicle-borne Sensor Network with Finite-Time Convergence
Intelligent vehicles in autonomous driving and obstacle avoidance, the precise relative state of vehicles put forward a higher demand.
A New Implementation of Federated Learning for Privacy and Security Enhancement
Nevertheless, since it is the model instead of the raw data that is shared, the system can be exposed to the poisoning model attacks launched by malicious clients.
Spatial Transformer Network with Transfer Learning for Small-scale Fine-grained Skeleton-based Tai Chi Action Recognition
Human action recognition is a quite hugely investigated area where most remarkable action recognition networks usually use large-scale coarse-grained action datasets of daily human actions as inputs to state the superiority of their networks.
User Scheduling for Federated Learning Through Over-the-Air Computation
A new machine learning (ML) technique termed as federated learning (FL) aims to preserve data at the edge devices and to only exchange ML model parameters in the learning process.
DT-Net: A novel network based on multi-directional integrated convolution and threshold convolution
To solve the above problems, in this paper, we propose a novel end-to-end semantic segmentation algorithm, DT-Net, and use two new convolution strategies to better achieve end-to-end semantic segmentation of medical images.
Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval
In light of these problems, we observed that most online content platforms have both a search and a recommender system that, while having heterogeneous input spaces, can be connected through their common output item space and a shared semantic representation.
Scheduling Policy and Power Allocation for Federated Learning in NOMA Based MEC
Due to the limited bandwidth, only a subset of connected devices can be scheduled in each round.
A New Multiple Max-pooling Integration Module and Cross Multiscale Deconvolution Network Based on Image Semantic Segmentation
We use the output feature maps from the multiple max-pooling integration module as the input of the decoder network; the multiscale convolution of each submodule in the decoder network is cross-fused with the feature maps generated by the corresponding multiscale convolution in the encoder network.
Adaptive Federated Learning With Gradient Compression in Uplink NOMA
Federated learning (FL) is an emerging machine learning technique that aggregates model attributes from a large number of distributed devices.
Networking and Internet Architecture Signal Processing
Deep Image Feature Learning with Fuzzy Rules
The method progressively learns image features through a layer-by-layer manner based on fuzzy rules, so the feature learning process can be better explained by the generated rules.
