Residual Connections & MLP Blocks
When building deep neural networks, one of the most significant challenges is ensuring that information and gradients can flow through many layers without being lost or distorted. As networks become deeper, they are prone to issues like vanishing or exploding gradients, making them difficult to train and potentially leading to poor performance. To address this, transformers employ residual connections — also called skip connections — which allow the input to a layer to bypass the layer and be added directly to its output. This design enables each layer to learn only the necessary modifications to the input, rather than having to learn the entire transformation from scratch. By preserving the original information and facilitating gradient flow, residual connections make it possible to train very deep models that converge more reliably and achieve higher accuracy.
Layer normalization is a technique that standardizes the inputs to a layer for each training example, stabilizing the distribution of activations and gradients. By normalizing across the features of each input, layer normalization helps maintain training stability, accelerates convergence, and reduces sensitivity to hyperparameter choices.
Within each transformer layer, you will also find a multi-layer perceptron (MLP) block — sometimes referred to as a feedforward block. After the self-attention mechanism processes the input, the output passes through a series of fully connected linear transformations with a non-linear activation function in between (commonly GELU or ReLU). The MLP block enables the model to capture and transform complex feature interactions that are not directly modeled by attention alone. This combination of attention and MLP layers, each wrapped with residual connections and normalization, forms the backbone of the transformer architecture, allowing it to learn rich, hierarchical representations from data.
Transformers with residual connections can be stacked much deeper than those without, as skip connections prevent the degradation of information and gradients. Without residuals, deeper models often fail to train effectively.
Skip connections, together with normalization, stabilize the training process by ensuring that signals and gradients do not vanish or explode as they pass through many layers.
Models with residual connections converge faster and more reliably during training. In contrast, transformers without skip connections may experience stalled or unstable learning, especially as depth increases.
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Can you explain how residual connections help with gradient flow in deep networks?
What is the main purpose of the MLP block in a transformer layer?
How do attention mechanisms and MLP blocks work together in transformers?
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When building deep neural networks, one of the most significant challenges is ensuring that information and gradients can flow through many layers without being lost or distorted. As networks become deeper, they are prone to issues like vanishing or exploding gradients, making them difficult to train and potentially leading to poor performance. To address this, transformers employ residual connections — also called skip connections — which allow the input to a layer to bypass the layer and be added directly to its output. This design enables each layer to learn only the necessary modifications to the input, rather than having to learn the entire transformation from scratch. By preserving the original information and facilitating gradient flow, residual connections make it possible to train very deep models that converge more reliably and achieve higher accuracy.
Layer normalization is a technique that standardizes the inputs to a layer for each training example, stabilizing the distribution of activations and gradients. By normalizing across the features of each input, layer normalization helps maintain training stability, accelerates convergence, and reduces sensitivity to hyperparameter choices.
Within each transformer layer, you will also find a multi-layer perceptron (MLP) block — sometimes referred to as a feedforward block. After the self-attention mechanism processes the input, the output passes through a series of fully connected linear transformations with a non-linear activation function in between (commonly GELU or ReLU). The MLP block enables the model to capture and transform complex feature interactions that are not directly modeled by attention alone. This combination of attention and MLP layers, each wrapped with residual connections and normalization, forms the backbone of the transformer architecture, allowing it to learn rich, hierarchical representations from data.
Transformers with residual connections can be stacked much deeper than those without, as skip connections prevent the degradation of information and gradients. Without residuals, deeper models often fail to train effectively.
Skip connections, together with normalization, stabilize the training process by ensuring that signals and gradients do not vanish or explode as they pass through many layers.
Models with residual connections converge faster and more reliably during training. In contrast, transformers without skip connections may experience stalled or unstable learning, especially as depth increases.
Merci pour vos commentaires !
