Gated Exponential Linear Unit

Author

Abhishek Kumar Pandey
May 18, 2024

Gated Exponential Linear Unit

Easy:

Alright, imagine you have a toy that can help you jump really high. But, before you use the toy, you need to check two things: if you’re allowed to jump and how much power you need for the jump.

The Gated Exponential Linear Unit (GELU) works a bit like this toy for a computer’s brain, which we call a neural network. The neural network learns to do things like recognize pictures or understand speech.

Here’s how it works in simple terms:

  1. Check Permission (the “Gate”): Before the neural network decides to use the information it has, it checks if it’s a good idea. It’s like asking, “Am I allowed to jump?” If the answer is yes, it moves to the next step.

  2. Prepare Power (the “Exponential Linear Unit”): If it gets permission, it figures out how much power it needs to use. This step uses a special math formula that’s a bit like stretching a rubber band — not too loose, not too tight, just the right amount to make a good jump.

Combining these steps, GELU helps the neural network decide how strongly to react to the information it gets, kind of like making sure our toy jump is just right — not too high and not too low.

So, GELU helps the computer’s brain make better decisions by carefully deciding when and how much to “jump” or react to the information it’s processing.

Moderate:

A Gated Exponential Linear Unit (GELU) is a type of activation function used in neural networks, which are computing systems inspired by the human brain. Neural networks learn from data to improve their performance over time. An activation function determines how a neuron responds to input signals. The choice of activation function can significantly impact the learning capability of a neural network.

The GELU stands out because it combines the benefits of both linear and non-linear operations. Here’s a breakdown of what it does:

  1. Linear Operation: In its simplest form, an activation function could just multiply the input by a constant factor. This is a linear operation, meaning the output is directly proportional to the input. However, this doesn’t allow the model to learn complex patterns.

  2. Non-Linear Operation: To overcome the limitations of linear functions, we use non-linear functions. These functions introduce complexity, allowing the model to learn from the errors it makes during training. A common example of a non-linear function is the sigmoid function, which squashes any real-valued number into a range between 0 and 1.

  3. Gating Mechanism: The “gated” part comes from adding a mechanism that decides whether to apply the linear or non-linear transformation based on some condition. This decision-making process is akin to having a gatekeeper who lets certain inputs through but blocks others. In the context of GELU, this gating mechanism allows the function to adaptively choose between linear and non-linear transformations, making it more flexible and powerful.

  4. Exponential Growth: The term “exponential” refers to the fact that the function can grow very quickly as the input increases. This property is particularly useful in deep learning models where layers of neurons process information, and the ability to rapidly increase the signal strength can help the model learn faster and more accurately.

  5. Why It’s Important: By combining these elements — linear operations, non-linearity, adaptive gating, and exponential growth — the GELU activation function enables neural networks to learn more effectively from data. It helps the network capture complex patterns and relationships in the data, leading to better performance on various tasks such as image recognition, natural language processing, and many others.

In summary, a Gated Exponential Linear Unit is a sophisticated activation function that uses a combination of linear and non-linear operations, along with a gating mechanism, to enhance the learning capabilities of neural networks. Its design allows for efficient and effective learning from large datasets, contributing to the remarkable success of deep learning technologies

Hard:

The Gated Exponential Linear Unit (GELU) is an activation function used in neural networks. It’s designed to improve the flow of information through the network by selectively passing or suppressing values based on their magnitude. Here’s a breakdown:

Formula:

GELU(x) = x * Φ(x)

Where:

  • x is the input to the activation function.

  • Φ(x) is the cumulative distribution function (CDF) of the standard normal distribution. In simpler terms, it represents the probability that a randomly chosen value from a standard normal distribution is less than or equal to x.

How it Works:

Non-linearity: GELU introduces non-linearity to the network, allowing it to learn complex relationships between inputs and outputs.

Gating Mechanism: The multiplication of x by Φ(x) acts as a gating mechanism.

  • For large positive values of x, Φ(x) approaches 1, allowing the signal to pass through almost unchanged.

  • For large negative values of x, Φ(x) approaches 0, effectively suppressing the signal.

  • For values around zero, GELU acts like a weighted average, allowing a portion of the signal to pass through.

Advantages:

  • Smoothness: GELU is a smooth function, which can help with gradient-based optimization algorithms during training.

  • Adaptive Filtering: The gating mechanism allows GELU to adaptively filter information based on its magnitude, potentially improving the network’s ability to learn relevant features.

  • Empirical Success: GELU has shown good performance in various deep learning tasks, particularly in natural language processing.

Comparison to Other Activation Functions:

  • ReLU: GELU addresses the “dying ReLU” problem by allowing some information to pass through even for negative inputs.

  • Sigmoid/Tanh: GELU provides a wider range of output values compared to sigmoid and tanh, potentially improving the expressiveness of the network.

In Summary:

GELU is a powerful activation function that combines non-linearity with a gating mechanism to improve the flow of information in neural networks. Its smooth nature and adaptive filtering capabilities have contributed to its success in various deep learning applications.

A few books on deep learning that I am reading: