Hybrid Discrete-Continuous Geometric Deep Learning
Scalable and equivariant spherical CNNs by DISCO convolutions —  No existing spherical convolutional neural network (CNN) framework is both computationally scalable and rotationally equivariant. Continuous approaches capture rotational equivariance but are often prohibitively computationally demanding. Discrete approaches offer more favorable computational performance but at the cost of equivariance. We develop a hybrid discrete-continuous (DISCO) group convolution that is simultaneously…
Hybrid Discrete-Continuous Geometric Deep Learning Scalable and equivariant spherical CNNs by DISCO convolutions —  No existing spherical convolutional neural network (CNN) framework is both computationally scalable and rotationally equivariant. Continuous approaches capture rotational equivariance but are often prohibitively computationally demanding. Discrete approaches offer more favorable computational performance but at the cost of equivariance. We develop a hybrid discrete-continuous (DISCO) group convolution that is simultaneously… deep-learning
Hybrid DiscreteContinuous Geometric Deep Learning Scalable equivariant spherical CNNs DISCO convolution — existing spherical convolutional neural network CNN framework computationally scalable rotationally equivariant Continuous approach capture rotational equivariance often prohibitively computationally demanding Discrete approach offer favorable computational performance cost equivariance develop hybrid discretecontinuous DISCO group convolution simultaneously… deeplearning