In this paper, we study the problem of unsupervised object segmentation from single images.

Until now, no tabular method has consistently outperformed classical supervised learning, which ignores these shifts.

TPP models in density estimation and strongly outperforms them in forecasting.

In this section, we discuss the hyperbolic operations used in HNN formulations and set up the meta-learning problem. This particular setup is also known as the N-ways K-shot learning problem. This section provides the theoretical proofs of the theorems presented in our main paper. Note that points in the local tangent space follow Euclidean algebra. The columns present the number of tasks in each batch (# Tasks), HNN update learning rate (), meta update learning rate (), and size of hidden dimensions (d).

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