Crossmem

Crossmem refers to a computational technique used in artificial intelligence and machine learning, specifically within the realm of memory augmentation for neural networks. The core idea behind crossmem is to enable a neural network to selectively access and utilize information from an external memory module in a more sophisticated way than simple read/write operations. Instead of relying solely on its internal parameters, a network employing crossmem can retrieve relevant pieces of information stored externally to aid in its decision-making process.

This external memory can be thought of as a database or a knowledge store. Crossmem mechanisms typically involve a controller network that determines what information to read from or write to this memory based on the current input and task. The "cross" in crossmem often alludes to the way the controller "cross-references" the input with the contents of the memory, identifying similarities or relationships to decide which memory slots are most pertinent. This allows the network to handle tasks that require long-term dependencies or access to a large corpus of information without needing to encode all of it within its weights. Applications can include question answering, text generation, and complex reasoning tasks.