Empirical results, however,suggest thatnetworks of moderate size already yield appealing performance. To explain such a gap, a common belief is that many data sets exhibit low dimensional structures, and can be modeled as samples near a low dimensional manifold.

However, existing quantization methods usually have inferior performancewhenusedonLSTMs.

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With acareful study, we find amore general problem which is rooted in low-frequency words in the text corpus.

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

We investigate a sequential optimization procedure to minimize the empiricalrisk functional fbθ(x) = 12kGbθ(x) yk2 for certain families of deep networks Gθ(x).

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.