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MazeNet: An Accurate, Fast, and Scalable Deep Learning Solution for Steiner Minimum Trees
- MazeNet is a deep learning-based method for solving the Obstacle Avoiding Rectilinear Steiner Minimum Tree (OARSMT) problem.
- MazeNet reframes OARSMT as a maze-solving task and utilizes a recurrent convolutional neural network (RCNN).
- MazeNet achieves perfect OARSMT-solving accuracy, reduces runtime compared to classical exact algorithms, and can handle more terminals than approximate algorithms.
- The scalability of MazeNet allows for training on small mazes and solving larger mazes by replicating pre-trained blocks.
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