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Found 13 papers, 4 papers with code
FedDIP: Federated Learning with Extreme Dynamic Pruning and Incremental Regularization
Federated Learning (FL) has been successfully adopted for distributed training and inference of large-scale Deep Neural Networks (DNNs).
Real time enhancement of operator's ergonomics in physical human - robot collaboration scenarios using a multi-stereo camera system
In collaborative tasks where humans work alongside machines, the robot's movements and behaviour can have a significant impact on the operator's safety, health, and comfort.
Optimizing Vision Transformers for Medical Image Segmentation
For medical image semantic segmentation (MISS), Vision Transformers have emerged as strong alternatives to convolutional neural networks thanks to their inherent ability to capture long-range correlations.
Improving Sequential Query Recommendation with Immediate User Feedback
We propose to augment the transformer-based causal language models for query recommendations to adapt to the immediate user feedback using multi-armed bandit (MAB) framework.
Max-Utility Based Arm Selection Strategy For Sequential Query Recommendations
We show that such a selection strategy often results in higher cumulative regret and to this end, we propose a selection strategy based on the maximum utility of the arms.
A Proactive Management Scheme for Data Synopses at the Edge
Our model can reveal the differences in the exchanged synopses and provide a datasets similarity map which becomes the appropriate knowledge base to support the desired processing activities.
Cooperative Multi-Modal Localization in Connected and Autonomous Vehicles
Cooperative Localization is expected to play a crucial role in various applications in the field of Connected and Autonomous vehicles (CAVs).
An Intelligent Edge-Centric Queries Allocation Scheme based on Ensemble Models
The combination of Internet of Things (IoT) and Edge Computing (EC) can assist in the delivery of novel applications that will facilitate end users activities.
Data Synopses Management based on a Deep Learning Model
An ecosystem of intelligent nodes is created at the EC giving the opportunity to support cooperative models.
On the Use of Interpretable Machine Learning for the Management of Data Quality
Our aim is to secure data quality, at least, for those features that are detected as significant in the collected datasets.
ML-AQP: Query-Driven Approximate Query Processing based on Machine Learning
As more and more organizations rely on data-driven decision making, large-scale analytics become increasingly important.
Databases
Adaptive Learning of Aggregate Analytics under Dynamic Workloads
Large organizations have seamlessly incorporated data-driven decision making in their operations.
Explaining Aggregates for Exploratory Analytics
Analysts wishing to explore multivariate data spaces, typically pose queries involving selection operators, i. e., range or radius queries, which define data subspaces of possible interest and then use aggregation functions, the results of which determine their exploratory analytics interests.
