Search Results for author:
Found 5 papers, 0 papers with code
Towards Addressing Training Data Scarcity Challenge in Emerging Radio Access Networks: A Survey and Framework
The extensive survey of training data scarcity addressing techniques combined with proposed framework to select a suitable technique for given type of data, can assist researchers and network operators in choosing appropriate methods to overcome the data scarcity challenge in leveraging AI to radio access network automation.
Coordinated Reinforcement Learning for Optimizing Mobile Networks
In this work, we demonstrate how to use coordination graphs and reinforcement learning in a complex application involving hundreds of cooperating agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
SimPose: Effectively Learning DensePose and Surface Normals of People from Simulated Data
Additionally, we present our model's 3D surface normal predictions on the MSCOCO dataset that lacks any real 3D surface normal labels.
Predicting the Present and Future States of Multi-agent Systems from Partially-observed Visual Data
We present a method which learns to integrate temporal information, from a learned dynamics model, with ambiguous visual information, from a learned vision model, in the context of interacting agents.
Stochastic Prediction of Multi-Agent Interactions from Partial Observations
We present a method that learns to integrate temporal information, from a learned dynamics model, with ambiguous visual information, from a learned vision model, in the context of interacting agents.
