28 citations · 61 across the 15 of their papers we have counts for
9 papers · 1 filter
Federated Learning in Mobile Networks: A Comprehensive Case Study on Traffic Forecasting
Nikolaos Pavlidis, Vasileios Perifanis, Selim F. Yilmaz +6
The increasing demand for efficient resource allocation in mobile networks has catalyzed the exploration of innovative solutions that could enhance the task of real-time cellular t…
Private Collaborative Edge Inference via Over-the-Air Computation
Selim F. Yilmaz, Burak Hasircioglu, Li Qiao +1
We consider collaborative inference at the wireless edge, where each client's model is trained independently on its local dataset. Clients are queried in parallel to make an accura…
Towards Energy-Aware Federated Traffic Prediction for Cellular Networks
Vasileios Perifanis, Nikolaos Pavlidis, Selim F. Yilmaz +6
Cellular traffic prediction is a crucial activity for optimizing networks in fifth-generation (5G) networks and beyond, as accurate forecasting is essential for intelligent network…
Over-the-Air Ensemble Inference with Model Privacy
Selim F. Yilmaz, Burak Hasircioglu, Deniz Gunduz
We consider distributed inference at the wireless edge, where multiple clients with an ensemble of models, each trained independently on a local dataset, are queried in parallel to…
PySAD: A Streaming Anomaly Detection Framework in Python
Selim F. Yilmaz, Suleyman S. Kozat
Streaming anomaly detection requires algorithms that operate under strict constraints: bounded memory, single-pass processing, and constant-time complexity. We present PySAD, a com…
Multi-Label Sentiment Analysis on 100 Languages with Dynamic Weighting for Label Imbalance
Selim F. Yilmaz, E. Batuhan Kaynak, Aykut Koç +2
We investigate cross-lingual sentiment analysis, which has attracted significant attention due to its applications in various areas including market research, politics and social s…
Unsupervised Anomaly Detection via Deep Metric Learning with End-to-End Optimization
Selim F. Yilmaz, Suleyman S. Kozat
We investigate unsupervised anomaly detection for high-dimensional data and introduce a deep metric learning (DML) based framework. In particular, we learn a distance metric throug…
Achieving Online Regression Performance of LSTMs with Simple RNNs
N. Mert Vural, Fatih Ilhan, Selim F. Yilmaz +2
Recurrent Neural Networks (RNNs) are widely used for online regression due to their ability to generalize nonlinear temporal dependencies. As an RNN model, Long-Short-Term-Memory N…
RNN-based Online Learning: An Efficient First-Order Optimization Algorithm with a Convergence Guarantee
N. Mert Vural, Selim F. Yilmaz, Fatih Ilhan +1
We investigate online nonlinear regression with continually running recurrent neural network networks (RNNs), i.e., RNN-based online learning. For RNN-based online learning, we int…