Understanding Memory Network: Improving Neural Networks with Extended Memory
With the advent of artificial intelligence, neural networks have proved to be extremely useful in various fields such as speech recognition, image classification, and natural language processing. However, most traditional neural networks lack a long-term memory component, which can hinder their performance. Memory Network is a novel architecture that aims to address the limitations of traditional neural networks by using an external memory component.
A Memory Network can read and write from a memory component that has the inference capabilities of a neural network model. The memory component comprises an array of objects indexed by mi and four potentially learned components: Input Feature Map (I), Generalization (G), Output Feature Map (O), and Response (R).
How Does Memory Network Work?
Given an input x, Memory Network works by following four steps:
- Convert x to an internal feature representation I(x): Memory Network takes input x, which can be a character, word or sentence, image or audio signal, and converts it to an internal feature representation I(x).
- Update memories mi given the new input: Memory Network updates memories mi by using the Generalization component G, which updates old memories with new input. The update formula is given as mi = G(mi, I(x), m), where m is the entire memory array.
- Compute output features o given the new input and the memory: Memory Network computes the output features by using the Output Feature Map component O. The formula is given as o = O(I(x), m).
- Decode output features o to give the final response: Memory Network decodes the output features o into the desired response by using the Response component R. The formula is given as r = R(o).
The process is applied at both training and test time, and memories are also stored at test time. However, the model parameters of I, G, O, and R are not updated.
Benefits of Memory Network
The Memory Network architecture has numerous benefits over traditional neural networks:
- Long-term memory storage: Memory Network has the capability to store long-term memory, which is not present in traditional neural networks like RNNs. This makes it a more efficient and effective tool for tasks that require memorizing tasks and copying.
- Compartmentalized memory organization: The memories in Memory Network are compartmentalized and structured, which makes it easier to retrieve information and make effective decisions.
- Inference capabilities of neural networks: Memory Network has the inference capabilities of neural networks, which makes it better at cognitive tasks like question answering, image captioning, and natural language understanding.
Memory Network is a novel architecture that combines the benefits of neural networks and long-term memory to make intelligent machines that can reason, understand and learn effectively. Its ability to store, compartmentalize and structure memory makes it a useful tool for a wide range of cognitive tasks that require reasoning, comprehension, and decision-making. Memory Network is one of the recent advances in the field of artificial intelligence that can have a significant impact on various industries that rely on intelligent machines for better decision making and productivity.
