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We systematically explored a spectrum of normalization algorithms related toBatch Normalization (BN) and propose a generalized formulation thatsimultaneously solves two major limitations of BN: (1) online learning and (2)recurrent learning. Our proposal is simpler and more biologically-plausible.Unlike previous approaches, our technique can be applied out of the box to alllearning scenarios (e.g., online learning, batch learning, fully-connected,convolutional, feedforward, recurrent and mixed --- recurrent andconvolutional) and compare favorably with existing approaches. We also proposeLp Normalization for normalizing by different orders of statistical moments. Inparticular, L1 normalization is well-performing, simple to implement, fast tocompute, more biologically-plausible and thus ideal for GPU or hardwareimplementations.

Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning