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Found 7 papers, 3 papers with code
Learning to Optimise Wind Farms with Graph Transformers
This work proposes a novel data-driven model capable of providing accurate predictions for the power generation of all wind turbines in wind farms of arbitrary layout, yaw angle configurations and wind conditions.
End-to-end Wind Turbine Wake Modelling with Deep Graph Representation Learning
Wind turbine wake modelling is of crucial importance to accurate resource assessment, to layout optimisation, and to the operational control of wind farms.
Efficient Offline Policy Optimization with a Learned Model
Instead of planning with the expensive MCTS, we use the learned model to construct an advantage estimation based on a one-step rollout.
Confidence Propagation Cluster: Unleash Full Potential of Object Detectors
It has been a long history that most object detection methods obtain objects by using the non-maximum suppression (NMS) and its improved versions like Soft-NMS to remove redundant bounding boxes.
From Image to Imuge: Immunized Image Generation
At the recipient's side, the verifying network localizes the malicious modifications, and the original content can be approximately recovered by the decoder, despite the presence of the attacks.
Learning Unmanned Aerial Vehicle Control for Autonomous Target Following
While deep reinforcement learning (RL) methods have achieved unprecedented successes in a range of challenging problems, their applicability has been mainly limited to simulation or game domains due to the high sample complexity of the trial-and-error learning process.
Transferring Rich Feature Hierarchies for Robust Visual Tracking
To fit the characteristics of object tracking, we first pre-train the CNN to recognize what is an object, and then propose to generate a probability map instead of producing a simple class label.
