Search Results for author:
Found 4 papers, 2 papers with code
Intelligent Resource Allocation in Dense LoRa Networks using Deep Reinforcement Learning
The anticipated increase in the count of IoT devices in the coming years motivates the development of efficient algorithms that can help in their effective management while keeping the power consumption low.
Examining Machine Learning for 5G and Beyond through an Adversarial Lens
Spurred by the recent advances in deep learning to harness rich information hidden in large volumes of data and to tackle problems that are hard to model/solve (e. g., resource allocation problems), there is currently tremendous excitement in the mobile networks domain around the transformative potential of data-driven AI/ML based network automation, control and analytics for 5G and beyond.
Efficient Urdu Caption Generation using Attention based LSTM
Our research aims to fill this gap by developing an attention-based deep learning model using techniques of sequence modeling specialized for the Urdu language.
Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning
Deep Reinforcement Learning (DRL) has numerous applications in the real world thanks to its outstanding ability in quickly adapting to the surrounding environments.
