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.
Blockchain technology is best known as the computer architecture on which Bitcoin and other cryptocurrencies are based, and it is also known as the [...]
Read More »In today's oversaturated information market, it is becoming increasingly difficult to retain users. For companies, competition is increasingly [...]
Read More »Credit scoring is a system used to rate credits and thus try to automate the decision making process at the time of purchasing a loan, and to [...]
Read More »The Official Chamber of Commerce of Seville, in collaboration with the Spanish Institute of Financial Analysts (IEAF), offered last March 16th [...]
Read More »
