Search Results for author:
Found 9 papers, 2 papers with code
MSTAR: Multi-Scale Backbone Architecture Search for Timeseries Classification
Most of the previous approaches to Time Series Classification (TSC) highlight the significance of receptive fields and frequencies while overlooking the time resolution.
Interference Cancellation GAN Framework for Dynamic Channels
Symbol detection is a fundamental and challenging problem in modern communication systems, e. g., multiuser multiple-input multiple-output (MIMO) setting.
Contextual Model Aggregation for Fast and Robust Federated Learning in Edge Computing
However, existing algorithms face issues with slow convergence and/or robustness of performance due to the considerable heterogeneity of data distribution, computation and communication capability at the edge.
On-the-fly Resource-Aware Model Aggregation for Federated Learning in Heterogeneous Edge
Edge computing has revolutionized the world of mobile and wireless networks world thanks to its flexible, secure, and performing characteristics.
Adversarial Neural Networks for Error Correcting Codes
To demonstrate the performance of our framework, we combine it with the very recent neural decoders and show improved performance compared to the original models and traditional decoding algorithms on various codes.
Anomaly Detection in Large Labeled Multi-Graph Databases
Within a large database G containing graphs with labeled nodes and directed, multi-edges; how can we detect the anomalous graphs?
Fast-Convergent Federated Learning
In this paper, we propose a fast-convergent federated learning algorithm, called FOLB, which performs intelligent sampling of devices in each round of model training to optimize the expected convergence speed.
Fidelius: Protecting User Secrets from Compromised Browsers
In this work, we present Fidelius, a new architecture that uses trusted hardware enclaves integrated into the browser to enable protection of user secrets during web browsing sessions, even if the entire underlying browser and OS are fully controlled by a malicious attacker.
Cryptography and Security
Cost-aware Targeted Viral Marketing in billion-scale networks
In this paper, we propose a new problem, called Cost-aware Targeted Viral Marketing (CTVM), to find the most cost-effective seed users who can influence the most relevant users to the advertisement.
