MPLL

MPLL refers to a type of neural network architecture known as a Memory-Augmented Neural Network (MANN). These networks are designed to enhance the capabilities of traditional neural networks by incorporating external memory modules. This allows them to store and retrieve information over extended periods, addressing limitations in learning long-term dependencies.

The core concept behind MPLLs is to decouple computation from storage. While a standard neural network processes information sequentially and relies on its internal weights for memory, an MPLL can access and manipulate data stored in a separate memory component. This external memory can be a matrix or a set of vectors, which the network learns to read from and write to.

The "MPLL" designation itself is often associated with specific implementations or research papers that explore these memory-augmented architectures. These models have shown promise in tasks that require remembering complex sequences or maintaining state over many time steps, such as question answering, natural language understanding, and reinforcement learning. The ability to access and process a larger history of information, beyond what can be held in standard recurrent neural network gates, is a key advantage.