What are the key differences between LSTM and memory-augmented networks?

What are the key differences between LSTM and memory-augmented networks?

LSTMs are a type of recurrent neural network designed to remember information for long periods, while memory-augmented networks can access external memory to store and retrieve information dynamically.

Can you explain how LSTMs handle long-term dependencies?

LSTMs use a gating mechanism to control the flow of information, allowing them to retain or forget information over time, which helps in managing long-term dependencies.

What are some applications of memory-augmented networks?

Memory-augmented networks are used in tasks like question answering, reinforcement learning, and any application requiring flexible memory usage.

Which one is better for natural language processing tasks?

It depends on the specific task. LSTMs are great for sequential data, while memory-augmented networks can excel in tasks requiring dynamic memory access.