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Found 7 papers, 0 papers with code
Addressing Bias Through Ensemble Learning and Regularized Fine-Tuning
Addressing biases in AI models is crucial for ensuring fair and accurate predictions.
Combinatorial Stochastic-Greedy Bandit
We propose a novel combinatorial stochastic-greedy bandit (SGB) algorithm for combinatorial multi-armed bandit problems when no extra information other than the joint reward of the selected set of $n$ arms at each time step $t\in [T]$ is observed.
FilFL: Client Filtering for Optimized Client Participation in Federated Learning
Federated learning is an emerging machine learning paradigm that enables clients to train collaboratively without exchanging local data.
Randomized Greedy Learning for Non-monotone Stochastic Submodular Maximization Under Full-bandit Feedback
We investigate the problem of unconstrained combinatorial multi-armed bandits with full-bandit feedback and stochastic rewards for submodular maximization.
Bridging the Urban-Rural Connectivity Gap through Intelligent Space, Air, and Ground Networks
Connectivity in rural areas is one of the main challenges of communication networks.
Artificial Intelligence for Satellite Communication: A Review
Satellite communication offers the prospect of service continuity over uncovered and under-covered areas, service ubiquity, and service scalability.
An original framework for Wheat Head Detection using Deep, Semi-supervised and Ensemble Learning within Global Wheat Head Detection (GWHD) Dataset
We emphasize on optimizing the performance of our proposed final architectures.
