Mar 4, 2024 · Defined by the formula GeGLU (x) = x sigmoid (x) + x 0.5 (1 + tanh [sqrt (2/pi) (x + 0.044715 x³)]). It marries the capabilities of GLU and GELU activations, offering...

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 …

Apr 18, 2019 · I was going through BERT paper which uses GELU (Gaussian Error Linear Unit) which states equation as $$ GELU (x) = xP (X ≤ x) = xΦ (x).$$ which in turn is approximated to $$0.5x (1 + tanh [\sqrt { 2/π} (...

GeGLU is an activation function which is a variant of GLU. The definition is as follows: GeGLU (x, W, V, b, c) = GELU (x W + b) ⊗ (x V + c) Source: GLU Variants Improve Transformer

Apr 18, 2022 · GELU is the activation that has been used in the GPT large language models by OpenAI. So as shown earlier in the feed-forward pass, GELU was used in place of the ReLU.

The GeGLU activation function builds upon the capabilities of both Gated Linear Units (GLU) and Generalized Linear Units (GELU) through a specific mathematical formula.

Mar 4, 2024 · Defined by the formula GeGLU (x) = x sigmoid (x) + x 0.5 (1 + tanh [sqrt (2/pi) (x + 0.044715 x^3)]). It marries the capabilities of GLU and GELU activations, offering a unique mechanism for controlling the flow of information through the network.

The Gaussian Error Linear Unit, or GELU, is an activation function. The GELU activation function is x Φ (x), where Φ (x) the standard Gaussian cumulative distribution function. The GELU nonlinearity weights inputs by their percentile, rather than …

Mar 12, 2023 · Heldout-set log-perplexity for Transformer models on the segment-filling task from T5. The GEGLU and SwiGLU variants produce the best perplexities.

The Gaussian Error Linear Unit (GELU) activation function is a popular choice in state-of-the-art models such as BERT, GPT, and Vision Transformers. This webpage offers a code tutorial for implementing GELU in Python, Tensorflow, and PyTorch.