Sparse networks refer to artificial neural networks that have a relatively low number of connections between the neurons. In contrast to dense networks, where every neuron is connected to every other neuron in the layer, sparse networks have only a fraction of those connections.

Benefits

Sparse networks are often used in deep learning because they have 2 main benefits over dense networks:

  1. They require fewer parameters to train, which reduces the risk of overfitting and can improve generalization performance.
  2. Sparse networks can be more computationally efficient than dense networks, making them better suited for deployment devices like mobile phone, VR headsets etc which have low storage and compute at their disposal.

Creating Sparse NN

There are several ways to create sparse networks.

  1. Randomly prune a dense network after training, removing a certain percentage of the connections between neurons.
  2. Design a network architecture with sparse connectivity patterns, such as the Inception network, which uses multiple parallel paths with sparse connections between them.

Examples

Some places where Sparse NN is used:

  1. Image recognition: Sparse convolutional neural networks (CNNs) have been shown to achieve state-of-the-art performance on image classification tasks, while requiring fewer parameters than dense CNNs. Sparse CNNs are also more computationally efficient, making them suitable for deployment on mobile devices.

  2. Speech recognition: Sparse autoencoders can be used to extract high-level features from speech signals, which can then be used for speech recognition tasks. By using sparsity constraints during training, autoencoders can learn more compact representations of the speech signal, reducing the risk of overfitting and improving generalization performance.

  3. Natural language processing: Sparse word embeddings can be used to represent words in a text corpus as high-dimensional vectors with many zeros, indicating that the word does not appear frequently in the corpus. Sparse embeddings can be more computationally efficient than dense embeddings, making them useful for large-scale language modeling tasks like information Retrieval.

  4. Reinforcement learning: Sparse reward signals can be used to train agents in reinforcement learning tasks, where the agent must learn to take actions in an environment to maximize a reward signal. By using sparse rewards, the agent must learn to explore the environment more efficiently and discover new strategies to achieve its goals.

Overall, sparse networks have shown promising results in a variety of applications and I expect even more innovative uses of sparse networks in the future.