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.
Industry 4.0 or the Fourth Industrial Revolution is based on the integration of digital technologies in the production and processing of goods and services.
Read More »As a consequence of this pandemic and economic situation in which we have found ourselves for the last two years, with the intention of better protecting the [...]
Read More »Hoy, 3 de octubre, hemos estado en los prestigiosos "Premios SCALEUPS B2B organizada por la Fundación Empresa y Sociedad, para hablaros de la Medici [...]
Read More »The banking sector has undergone considerable transformations over the past 10 years. Especially as banking has become more integrated and [...]
Read More »
