Inrealisticscenarios with very limited numbers of data samples, it can be challenging to identify a kernel powerful enough to distinguish complex distributions.

The resulting agent outperforms RainbowonAtari, installing anewState oftheArtwith very little modifications to the original algorithm.

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

Using the method of auxiliary coordinates, we derive a training algorithm that works by alternating steps that train an auxiliary embeddingwith steps that train the mapping.

A core task in multi-modal learning is to integrate information from multiple feature spaces (e.g., text and audio), offering modality-invariant essential representationsofdata.

In the classical decision-making literature, this is achieved by two interweaving processes, policyevaluation and policyimprovement (Sutton and Barto,2018).

Our novel setup brings new understandings to both disentanglement and equivariance .

But how to explain this phenomenon has been largely ignored by existing studies.

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