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Found 11 papers, 7 papers with code
Distributed Deep Joint Source-Channel Coding with Decoder-Only Side Information
We consider low-latency image transmission over a noisy wireless channel when correlated side information is present only at the receiver side (the Wyner-Ziv scenario).
High Perceptual Quality Wireless Image Delivery with Denoising Diffusion Models
We consider the image transmission problem over a noisy wireless channel via deep learning-based joint source-channel coding (DeepJSCC) along with a denoising diffusion probabilistic model (DDPM) at the receiver.
Towards Energy-Aware Federated Traffic Prediction for Cellular Networks
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 design, resource allocation and anomaly mitigation.
Distributed Deep Joint Source-Channel Coding over a Multiple Access Channel
We consider distributed image transmission over a noisy multiple access channel (MAC) using deep joint source-channel coding (DeepJSCC).
Federated Spatial Reuse Optimization in Next-Generation Decentralized IEEE 802.11 WLANs
As wireless standards evolve, more complex functionalities are introduced to address the increasing requirements in terms of throughput, latency, security, and efficiency.
Over-the-Air Ensemble Inference with Model Privacy
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 make an accurate decision on a new sample.
PySAD: A Streaming Anomaly Detection Framework in Python
PySAD is an open-source python framework for anomaly detection on streaming data.
Multi-Label Sentiment Analysis on 100 Languages with Dynamic Weighting for Label Imbalance
We investigate cross-lingual sentiment analysis, which has attracted significant attention due to its applications in various areas including market research, politics and social sciences.
Achieving Online Regression Performance of LSTMs with Simple RNNs
Recurrent Neural Networks (RNNs) are widely used for online regression due to their ability to generalize nonlinear temporal dependencies.
Unsupervised Anomaly Detection via Deep Metric Learning with End-to-End Optimization
We investigate unsupervised anomaly detection for high-dimensional data and introduce a deep metric learning (DML) based framework.
RNN-based Online Learning: An Efficient First-Order Optimization Algorithm with a Convergence Guarantee
We investigate online nonlinear regression with continually running recurrent neural network networks (RNNs), i. e., RNN-based online learning.
