A PyTorch implementation for Transition Matrix Representation of Trees with Transposed Convolutions
This project is a PyTorch implementation for Transition Matrix Representation of Trees with Transposed Convolutions, published as a conference proceeding atSDM 2022. The paper proposes TART (Transition Matrix Representation withTransposed Convolutions), a novel framework for generalizing tree models with aunifying view. Requirements The repository is written by Python 3.7 with the packages listed inrequirements.txt. A GPU environment is strongly recommended for efficienttraining and inference of our model. You can type the following command toinstall the required packages: pip install -r […]
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