Scaling model capacity has been vital in the success of deep learning.

Introspection reveals that our meta learned LAs learn through fast association in a way that is qualitatively different from gradientdescent.

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

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We introduce a visual challenge, Pathfinder, and describe a novel recurrent neural network architecture called the horizontal gated recurrent unit (hGRU) to learn intrinsic horizontal connections - both within and across feature columns.

The T = 100, itisclearthatT grows.SABstill tocompleteT = 5000, whereasT = 2000bothv self-attention 1/8 = 12.5%).

This sampling process is embodied as the policyπ, which is responsible for outputting an actiona conditioned on past environmental states{s}.

Adaptive parameterization is a means of increasing this flexibility and thereby increasing the model's capacity to learn non-linear patterns. We focus on the feed-forward layer, f(x):= φ(W x+b),for some activation functionφ: R 7 R. Define the pre-activation layer as a = A(x):= Wx+band denote byg(a):= φ(a)/athe activation effect ofφgivena, where divisioniselement-wise.