Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for online automatic hyperparameter tuning of a machine learning model that provides a user experience to media devices such that the machine learning model maximizes (or minimizes) an objective function. An example embodiment operates by generating an initial set of hyperparameter configurations for a machine learning model based on sampling data received from media devices over a network. The embodiment then determines, using an hyperparameter tuning method, a hyperparameter configuration based on the initial set of hyperparameter configurations that causes a training of the machine learning model using a learning algorithm to maximize an objective function. The embodiment then trains the machine learning model according to the determined hyperparameter configuration using the learning algorithm.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for candidate ranking for content recommendation. An embodiment operates by receiving category candidates over a network, wherein each of the category candidates comprises content candidates associated with one or more applications operating on media devices. The embodiment then ranks the category candidates based on a machine model trained using a learning algorithm based on the time series data, and ranks the content candidates in the each of category candidates based on the time series data. The embodiment then causes the ranked category candidates and the ranked content candidates to be outputted for display.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for stochastic candidate selection for content recommendation. An example embodiment operates by a computer-implemented method for stochastic candidate selection for content recommendation. The method includes receiving, by at least one computer processor, a first plurality of content candidates and selecting a second plurality of content candidates from the first plurality of content candidates. The method further include ranking the second plurality of content candidates based on one or more parameters and selecting a third plurality of content candidates from the ranked second plurality of content candidates. The method can further include displaying the third plurality of content candidates using a display device.

Abstract: Disclosed herein are various embodiments, for a content display and clustering system. An example embodiment operates by receiving a request to display the plurality of content items. At each of multiple levels different pairs of content items are identified and a similarity score is computed for each pair. A subset of pairs for which their similarity score exceeds a similarity threshold for the respective level are identified and clustered. This process is repeated for one or more iterations at the same level, and then the process is repeated for each of the multiple levels. A final clustered subset is identified, and output for display, responsive to the request to display the plurality of content items.

Abstract: Disclosed herein are various embodiments, for a content display and clustering system. An example embodiment operates by receiving a request to display the plurality of content items. At each of multiple levels different pairs of content items are identified and a similarity score is computed for each pair. A subset of pairs for which their similarity score exceeds a similarity threshold for the respective level are identified and clustered. This process is repeated for one or more iterations at the same level, and then the process is repeated for each of the multiple levels. A final clustered subset is identified, and output for display, responsive to the request to display the plurality of content items.

Abstract: Disclosed are mechanisms for selecting a recommended item for a current item being viewed by a user account based on a view history of the user account with reduced bias. For a current item being viewed by the user account represented by a current node of a co-watch graph, embodiments can select a recommended item represented by an associated node in the co-watch graph likely being viewed by the user account, and determine a probability of the recommended item likely being viewed. The co-watch graph can be generated based on a view history of the user account. An edge between a first node and a second node of the co-watch graph can have a weight representing a number of co-occurrence times when the first item represented by the first node and the second item represented by the second node are viewed in sequence within a predetermined time interval.

Abstract: Example parameter optimization methods and apparatus are described. In one example parameter optimization method, a data processing device obtains a Kronecker factor matrix that is used to indicate a higher-order information matrix of a neural network model, and segments the Kronecker factor matrix to obtain a plurality of square matrices. The obtained plurality of square matrices are submatrices of the Kronecker factor matrix, and main diagonal lines of the plurality of square matrices each are in a one-to-one correspondence with a part of a main diagonal line of the Kronecker factor matrix. Then, the data processing device adjusts a parameter of the neural network model based on the obtained plurality of square matrices.

Abstract: The present disclosure relates to parameter updating methods. In one example method, a parameter in a neural network model is updated for a plurality of times through a plurality of iterations. The plurality of iterations include a first iteration period and a second iteration period. In the first iteration period, an inverse matrix of an additional matrix of the neural network model is updated once based on a quantity of iterations indicated by a first update stride. In the second iteration period, the inverse matrix of the additional matrix of the neural network model is updated once based on a quantity of iterations indicated by a second update stride, where the first iteration of the second iteration period is after the last iteration of the first iteration period in an iteration sequence, and the second update stride is greater than the first update stride.

Abstract: A method and apparatus cast a material with nano-micro duplex grain structure. The apparatus includes module system, heating system casting mold and gating system, multiaxial compound motion system accompanied by the following technological characteristics; alloy smelting; after heat preservation, alloy melt is poured into the casting mold which is put into the centrifugal barrel of the six-axis motion system; then the casting mold carries out composite motion and the alloy melt starts solidification; as a result, casting Al—Si alloy block with multi-scale nano-structure includes nano-micro duplex grain group is prepared.

Abstract: A method and apparatus casts a material with nano-micro duplex grain structure. The apparatus is comprised of module system, heating system, casting mold and gating system, multiaxial compound motion system accompanied by the following technological characteristics; alloy smelting; after heat preservation, alloy melt are poured into the casting mold which is put into the centrifugal barrel of the six-axis motion system; then the casting mold carries out composite motion and the alloy melt starts solidification; as a result, casting Al—Si alloy block with multi-scale nano-structure comprising of nano-micro duplex grain group are prepared. The advantages of the present invention includes that multi-scale casting nano-structure with nano-micro duplex grain group are obtained because of the composite shear flow field generated in the alloy melt.

Abstract: Compositions containing an adhesive material, a release additive and a compatibilizer are suitable for temporarily bonding two surfaces, such as a wafer active side and a substrate. These compositions are useful in the manufacture of electronic devices where a temporary bonding of a component to a substrate is desired.

Abstract: Compositions useful for improving the adhesion of coating compositions, such as dielectric film-forming compositions, include a hydrolyzed poly(alkoxysilane). These compositions are useful in methods of improving the adhesion of coating compositions to a substrate.

Abstract: Arylcyclobutene polymers having improved toughness are provided. Compositions and methods for coating arylcyclobutene polymers having improved toughness are also provided.

Abstract: Arylcyclobutene polymers having improved toughness are provided. Compositions and methods for coating arylcyclobutene polymers having improved toughness are also provided.

Abstract: Compositions containing an adhesive material, a release additive and a compatibilizer are suitable for temporarily bonding two surfaces, such as a wafer active side and a substrate. These compositions are useful in the manufacture of electronic devices where a temporary bonding of a component to a substrate is desired.

Abstract: Compositions containing an adhesive material, a release additive and a compatibilizer are suitable for temporarily bonding two surfaces, such as a wafer active side and a substrate. These compositions are useful in the manufacture of electronic devices where a temporary bonding of a component to a substrate is desired.

Abstract: Arylcyclobutene polymers having improved toughness are provided. Compositions and methods for coating arylcyclobutene polymers having improved toughness are also provided.

Abstract: Compositions useful for improving the adhesion of coating compositions, such as dielectric film-forming compositions, include a hydrolyzed poly(alkoxysilane). These compositions are useful in methods of improving the adhesion of coating compositions to a substrate.

Abstract: Compositions useful for improving the adhesion of coating compositions, such as dielectric film-forming compositions, include a hydrolyzed poly(alkoxysilane). These compositions are useful in methods of improving the adhesion of coating compositions to a substrate.

Abstract: Compositions useful for improving the adhesion of coating compositions, such as dielectric film-forming compositions, include a hydrolyzed amino-alkoxysilane having a protected amino moiety. These compositions are useful in methods of improving the adhesion of coating compositions to a substrate, such as an electronic device substrate.