In this paper, we introduce a novel type of Rectified Linear Unit (ReLU), called a Dual Rectified Linear Unit (DReLU). A DReLU, which comes with an unbounded positive and negative image, can be used as a dropin replacement for a tanh activation function in the recurrent step of Quasi-...
Rectified Linear Units, or ReLUs, are a type of activation function that are linear in the positive dimension, but zero in the negative dimension. The kink in the function is the source of the non-linearity. Linearity in the positive dimension has the attractive property that it prevents non...
At first sight, ReLUs seem inappropriate for RNNs because they can have very large outputs so they might be expected to be far more likely to explode than units that have bounded values. — A Simple Way to Initialize Recurrent Networks of Rectified Linear Units, 2015. Nevertheless, there has...
dataset. Unlike binary units, rectified linear units preserve information about relative in- tensities as information travels through mul- tiple layers of feature detectors. 1. Introduction Restricted Boltzmann machines (RBMs) have been used as generative models of many different types ...
rectified units from: http://en.wikipedia.org/wiki/Rectifier_(neural_networks) In the context ofartificial neural networks, therectifieris anactivation functiondefined as Noisy ReLUs[edit] Rectified linear units can be extended to includeGaussian noise, making them noisy ReLUs, giving[3]...
We propose to estimate the signals via a convex program based on rectified linear units (ReLUs) for two different quantization schemes, namely one‐bit and uniform multi‐bit quantization. Assuming that the linear measurement process can be modelled by a sensing matrix with i.i.d. subgaussian ...