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Found 8 papers, 2 papers with code
Improving Efficiency of DNN-based Relocalization Module for Autonomous Driving with Server-side Computing
Our findings highlight the vital role of server-side offloading in DNN-based camera relocation for autonomous vehicles, and we also discuss the results of data fusion.
Revisiting Initializing Then Refining: An Incomplete and Missing Graph Imputation Network
With the development of various applications, such as social networks and knowledge graphs, graph data has been ubiquitous in the real world.
FedVeca: Federated Vectorized Averaging on Non-IID Data with Adaptive Bi-directional Global Objective
However, the clients' Non-Independent and Identically Distributed (Non-IID) data negatively affect the trained model, and clients with different numbers of local updates may cause significant gaps to the local gradients in each communication round.
A Secure and Efficient Multi-Object Grasping Detection Approach for Robotic Arms
However, with wide applications of deep learning in robotic arms, there are new challenges such as the allocation of grasping computing power and the growing demand for security.
A Novel Optimized Asynchronous Federated Learning Framework
How to find a balance between the model performance and the communication cost is a challenge in AFL.
Foreground Object Structure Transfer for Unsupervised Domain Adaptation
In practice, since the label of the target domain is not available, we use the clustering information of the source domain to assign pseudo labels to the target domain samples, and then according to the source domain data prior knowledge guides those positive features to maximum the inter-class distance between different classes and mimimum the intra-class distance.
Deep Fusion Clustering Network
Specifically, in our network, an interdependency learning-based Structure and Attribute Information Fusion (SAIF) module is proposed to explicitly merge the representations learned by an autoencoder and a graph autoencoder for consensus representation learning.
Traffic Flow Combination Forecasting Method Based on Improved LSTM and ARIMA
In this paper, we define the traffic data time singularity ratio in the dropout module and propose a combination prediction method based on the improved long short-term memory neural network and time series autoregressive integrated moving average model (SDLSTM-ARIMA), which is derived from the Recurrent Neural Networks (RNN) model.
