ガウス誤差線形ユニット（GELU）は 処理します commonly used in 深層学習 architectures, particularly in transformer models like BERT and GPT. It offers a smoother alternative to traditional 活性化関数 ReLU（整流線形ユニット）やシグモイドなどに比べてより滑らかな代替手段を提供します。
The mathematical formulation of GELU combines both linear and nonlinear elements, allowing it to model complex データのパターンをより効果的に捉えることができます。この関数は次のように表されます：
GELU(x) = x * P(X ≤ x) = x * 0.5 * (1 + erf(x / √2))
ここで、 erf denotes the error function, and P(X ≤ x) represents the 累積分布関数 of the standard normal distribution. This formulation means that GELU outputs a value that is zero for negative inputs and gradually increases for positive inputs, which helps in maintaining a flow of gradients during backpropagation.
One of the key advantages of GELU over ReLU is its ability to retain negative values, which can lead to better 学習ダイナミクス and more nuanced representations in the model. This characteristic helps reduce the risk of dead neurons, a common issue with ReLU where neurons can become inactive and stop learning.
Due to its mathematical properties and performance benefits, GELU has gained popularity in state-of-the-art models, contributing to advancements in 自然言語処理 NLP、コンピュータビジョン、その他のAIアプリケーション。