Multi-task Learning (MTL) and The Role of Activation Functions in Neural Networks [Train MLP With…

🌈 Abstract

The article explores two important concepts in deep learning: multi-task learning (MTL) and the role of activation functions in neural networks. It covers how MTL works by training a multi-layer perceptron (MLP) for binary and multi-class classification tasks, and how activation functions help neural networks learn complex patterns.

🙋 Q&A

[01] Multi-Task Learning (MTL)

1. What is multi-task learning (MTL)?

• MTL is a machine learning method where multiple related tasks are learned simultaneously, leveraging shared information among them to improve performance.
• Instead of training a separate model for each task, MTL trains a single model to handle multiple tasks.

2. What are the benefits and drawbacks of MTL? Benefits:

• Can improve the performance of individual tasks when they are related
• Acts as a regularizer, preventing the model from overfitting on a single task
• Can be seen as a form of transfer learning

Drawbacks:

• Conflicting gradients from different tasks can affect the learning process, making it challenging to balance the learning across tasks
• As the number of tasks increases, the complexity and computational cost of MTL can grow significantly

3. How does the MTL architecture work in the given example?

• The model has two hidden layers that act as a shared representation, learning jointly for both tasks.
• Each task then has its own separate hidden layer.
• The output layers are determined by the target of each task, with one layer for binary classification (heart disease) and another for multi-class classification (thalassemia).

4. Can you explain the code implementation of the MTL architecture?

• The MultiTaskNet class defines the MTL architecture with shared and task-specific layers.
• The forward method defines the forward pass of the model, where the shared layers are followed by the task-specific layers.
• The training loop optimizes the combined loss from both tasks using the criterion_thal and criterion_heart loss functions.

[02] Activation Functions

1. What is the role of activation functions in neural networks?

• Activation functions introduce non-linearity into the neural network, allowing it to learn complex patterns in the data.
• Without activation functions, the neural network can only learn linear relationships in the data.

2. How do ReLU and Leaky ReLU activation functions work?

• ReLU converts all negative numbers to zero, which can lead to the "dying neuron" problem where some neurons stop learning.
• Leaky ReLU addresses this issue by downscaling negative values instead of setting them to zero, allowing a small amount of the negative signal to pass through.

3. What happens when a neural network is trained without activation functions?

• Without activation functions, the neural network's output is a linear combination of the input data, and it cannot learn any non-linear relationships.
• The model's performance is significantly worse compared to a model with activation functions, as it cannot capture the complexities present in the data.
• The output of the neural network without activation functions is similar to the output of a linear regression model, which can only learn linear patterns.

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