Abstract: A disclosed example includes implementing a first worker instance and a second worker instance to operate in parallel running a first tuning operation via the first worker instance to tune first hyperparameters; running a second tuning operation via the second worker instance using a Bayesian-based optimization to determine a hyperparameter configuration to evaluate next; evaluating the hyperparameter configuration for an external model using a surrogate model; and selecting the hyperparameter configuration for the external model.

Abstract: A system and method includes receiving a tuning work request for tuning an external machine learning model; implementing a plurality of distinct queue worker machines that perform various tuning operations based on the tuning work data of the tuning work request; implementing a plurality of distinct tuning sources that generate values for each of the one or more hyperparameters of the tuning work request; selecting, by one or more queue worker machines of the plurality of distinct queue worker machines, one or more tuning sources of the plurality of distinct tuning sources for tuning the one or more hyperparameters; and using the selected one or more tuning sources to generate one or more suggestions for the one or more hyperparameters, the one or more suggestions comprising values for the one or more hyperparameters of the tuning work request.

Abstract: A system and method includes receiving a tuning work request for tuning an external machine learning model; implementing a plurality of distinct queue worker machines that perform various tuning operations based on the tuning work data of the tuning work request; implementing a plurality of distinct tuning sources that generate values for each of the one or more hyperparameters of the tuning work request; selecting, by one or more queue worker machines of the plurality of distinct queue worker machines, one or more tuning sources of the plurality of distinct tuning sources for tuning the one or more hyperparameters; and using the selected one or more tuning sources to generate one or more suggestions for the one or more hyperparameters, the one or more suggestions comprising values for the one or more hyperparameters of the tuning work request.

Abstract: A system and method includes receiving a tuning work request for tuning an external machine learning model; implementing a plurality of distinct queue worker machines that perform various tuning operations based on the tuning work data of the tuning work request; implementing a plurality of distinct tuning sources that generate values for each of the one or more hyperparameters of the tuning work request; selecting, by one or more queue worker machines of the plurality of distinct queue worker machines, one or more tuning sources of the plurality of distinct tuning sources for tuning the one or more hyperparameters; and using the selected one or more tuning sources to generate one or more suggestions for the one or more hyperparameters, the one or more suggestions comprising values for the one or more hyperparameters of the tuning work request.

Abstract: A system and method includes receiving a tuning work request for tuning an external machine learning model; implementing a plurality of distinct queue worker machines that perform various tuning operations based on the tuning work data of the tuning work request; implementing a plurality of distinct tuning sources that generate values for each of the one or more hyperparameters of the tuning work request; selecting, by one or more queue worker machines of the plurality of distinct queue worker machines, one or more tuning sources of the plurality of distinct tuning sources for tuning the one or more hyperparameters; and using the selected one or more tuning sources to generate one or more suggestions for the one or more hyperparameters, the one or more suggestions comprising values for the one or more hyperparameters of the tuning work request.

Abstract: A system and method includes receiving a tuning work request for tuning an external machine learning model; implementing a plurality of distinct queue worker machines that perform various tuning operations based on the tuning work data of the tuning work request; implementing a plurality of distinct tuning sources that generate values for each of the one or more hyperparameters of the tuning work request; selecting, by one or more queue worker machines of the plurality of distinct queue worker machines, one or more tuning sources of the plurality of distinct tuning sources for tuning the one or more hyperparameters; and using the selected one or more tuning sources to generate one or more suggestions for the one or more hyperparameters, the one or more suggestions comprising values for the one or more hyperparameters of the tuning work request.

Abstract: A system and method includes receiving a tuning work request for tuning an external machine learning model; implementing a plurality of distinct queue worker machines that perform various tuning operations based on the tuning work data of the tuning work request; implementing a plurality of distinct tuning sources that generate values for each of the one or more hyperparameters of the tuning work request; selecting, by one or more queue worker machines of the plurality of distinct queue worker machines, one or more tuning sources of the plurality of distinct tuning sources for tuning the one or more hyperparameters; and using the selected one or more tuning sources to generate one or more suggestions for the one or more hyperparameters, the one or more suggestions comprising values for the one or more hyperparameters of the tuning work request.

Abstract: A UV detector is designed to provide a photoresponse with a cutoff wavelength below a predetermined wavelength. The detector uses a sensor element having an active layer comprising a MgS component grown directly on a substrate. A thin layer metal layer is deposited over the active layer and forms a transparent Schottky metal layer.

Abstract: A UV detector is designed to provide a photoresponse with a cutoff wavelength below a predetermined wavelength. The detector uses a sensor element having an active layer comprising a MgS component grown directly on a substrate. A thin layer metal layer is deposited over the active layer and forms a transparent Schottky metal layer.

Abstract: The present invention relates to UV detectors comprising undoped Zn1-xMgxS as the UV responsive active material. Where x exceeds 0.3 the thickness of the active material must be below a critical value, for example if 0.30<x<1.00, and the active material is formed as a layer of a thickness t wherein 5000 Å≧t≧100 Å. A particularly preferred combination of x and thickness is x=0.57 and t≦1400 Å because at around these values the UV response of the active material is similar to the UV response of human skin.

Abstract: Visible-blind UV detectors are disclosed comprising an active layer of ZnSTe alloy. The Te composition can be varied to provide good lattice matching depending on the nature of the substrate (eg Si, GaP or GaAs) and a novel structure is provided to give high quantum efficiency. The invention also discloses UV detectors with an active layer of pure ZnS and with an active layer of ZnSSe.