GELU is specifically known for being a smooth approximation of the ReLU (Rectified Linear Unit) activation function, but with a key difference: it is based on the cumulative distribution function of the Gaussian distribution.

Aug 17, 2019 · gelu is smoother near zero and "is differentiable in all ranges, and allows to have gradients(although small) in negative range" which helps with this problem.

Feb 28, 2025 · In this article, we explained the GELU activation function and compared it with the popular ReLU activation function. Further, we described its benefits and discussed cases where it offers improved performance.

Jun 27, 2016 · We propose the Gaussian Error Linear Unit (GELU), a high-performing neural network activation function. The GELU activation function is $x\Phi (x)$, where $\Phi (x)$ the standard Gaussian cumulative distribution function. The GELU nonlinearity weights inputs by their value, rather than gates inputs by their sign as in ReLUs ($x\mathbf {1}_ {x>0}$).

Apr 11, 2019 · The authors that proposed GELU argue that it is a deterministic non-linearity that encapsulates a stochastic regularization effect. In the following, we’ll discuss the detailed intuition behind GELU so that the reader can independently assess the author’s argument.

A system for using RL to determine segment points for piecewise linear approximation of activation functions like SiLU(swish). Able to generate piecewise linear approximation functions given range and segment points. Uses Stable Baselines3. For ISOCC2023.

The NewGELUActivation class is an implementation of the Gaussian Error Linear Units (GELU) activation function. In PyTorch, activation functions are essential non-linear transformations that are applied on the input, typically after linear transformations, to introduce …

GeLU Solution: Linear behavior for large positive values, gradual suppression for negative values. Impact: Faster training and better convergence

Aug 18, 2024 · Gaussian Error Linear Unit (GELU) is an activation function used in machine learning. While it resembles the classic ReLU (Rectified Linear Unit), there are some key differences. ReLU is a piecewise linear function that outputs 0 for inputs less than 0, and outputs the input itself for inputs greater than 0.

Jul 21, 2019 · GELU aims to combine them. Also, a new RNN regularizer called Zoneout stochastically multiplies the input by 1. We want to merge all 3 functionalities by stochastically multiplying the input by 0...