Abstract: Hyperparameters for tuning a machine learning system may be optimized using Bayesian optimization with constraints. The hyperparameter optimization may be performed for a received training set and received constraints. Respective probabilistic models for the machine learning system and constraint functions may be initialized, then hyperparameter optimization may include iteratively identifying respective values for hyperparameters using analysis of the respective models performed using an acquisition function implementing entropy search on the respective models, training the machine learning system using the identified values to determine measures of accuracy and constraint metrics, and updating the respective models using the determined measures.

Abstract: Hyperparameters for tuning a machine learning system may be optimized using Bayesian optimization with constraints. The hyperparameter optimization may be performed for a received training set and received constraints. Respective probabilistic models for the machine learning system and constraint functions may be initialized, then hyperparameter optimization may include iteratively identifying respective values for hyperparameters using analysis of the respective models performed using an acquisition function implementing entropy search on the respective models, training the machine learning system using the identified values to determine measures of accuracy and constraint metrics, and updating the respective models using the determined measures.

Abstract: A computer-Implemented method, system, and computer program product for optimizing production of an industrial facility. The industrial facility is designed to produce a predefinable quantity of at least one product. A model trained by machine learning is provided at a first time and the trained model is executed at a second time following the first time to generate a rolling forecast for a predefinable time interval. The predefinable time interval begins after the second time and the rolling forecast forecasts for any time within the time interval a quantity of the at least one product to be produced at this time. The rolling forecast is further processed by means of a further model to calculate a reforecast on the basis of the rolling forecast.

Abstract: A composite time series forecasting model comprising a neural network sub-model and one or more state space sub-models corresponding to individual time series is trained. During training, output of the neural network sub-model is used to determine parameters of the state space sub-models, and a loss function is computed using the values of the time series and probabilistic values generated as output by the state space sub-models. A trained version of the composite model is stored.

Abstract: With respect to an input data set which contains observation records of a time series, a statistical model which utilizes a likelihood function comprising a latent function is generated. The latent function comprises a combination of a deterministic component and a random process. Parameters of the model are fitted using approximate Bayesian inference, and the model is used to generate probabilistic forecasts corresponding to the input data set.

Abstract: A needle bed (1) that comprises strips (4) in order to form needle channels (5), with the strips being configured in a comb-like manner. Between the individual teeth of this comb, recesses (28 through 37) are formed, said recesses being disposed to supply fluid to the needle channel (5) and to drain said fluid. The groove (12) that accommodates the strip (4) forms a distributor space where the distribution of the fluid to be supplied to the knitting tools takes place over a section of the strip (4), which section is preferably greater than half the length of the strip.

Abstract: A needle bed (1) that comprises strips (4) in order to form needle channels (5), said strips being configured in a comb-like manner. Between the individual teeth of this comb, recesses (28 through 37) are formed, said recesses being disposed to supply fluid to the needle channel (5) and to drain said fluid. The groove (12) that accommodates the strip (4) forms a distributor space where the distribution of the fluid to be supplied to the knitting tools takes place over a section of the strip (4), said section being preferably greater than half the length of said strip.