Limited memory machines are machine learning models that use an external memory to store relevant information during the learning process. These models are used to solve sequence learning problems where it is required to maintain a long-term memory of previous inputs. The external memory in limited memory machines can be a vector, an array or a random access memory (RAM). The memory is read and written by selective attention, which allows the model to focus on relevant parts of the memory during prediction. Memory-constrained machines are used in natural language processing applications, such as machine translation and text generation, as well as in time series prediction tasks, such as stock market forecasting. A popular example of a limited memory machine is the transform neural network (Transformer), which has been successfully used in a wide variety of natural language processing applications.
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