Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a function call for a function that calculates an attribute associated with a machine learning model. For each argument of the function call, the system identifies a parameter type of the argument, wherein the parameter type represents a type of data used with the machine learning model. The system also obtains a value accessor for retrieving features specific to the parameter type and obtains a value represented by the argument using the value accessor. The system then calculates the attribute by applying the function to the value and uses the attribute to execute the machine learning model.

Abstract: In an example embodiment, position bias and other types of bias may be compensated for by using two-phase training of a machine-learned model. In a first phase, the machine-learned model is trained using non-randomized training data. Since certain types of machine-learned models, such as those involving deep learning (e.g., neural networks) require a lot of training data, this allows the bulk of the training to be devoted to training using non-randomized training data. However, since this non-randomized training data may be biased, a second training phase is then used to revise the machine-learned model based on randomized training data to remove the bias from the machine-learned model. Since this randomized training data may be less plentiful, this allows the deep learning machine-learned model to be trained to operate in an unbiased manner without the need to generate additional randomized training data.

Abstract: In an example embodiment, position bias and other types of bias may be compensated for by using two-phase training of a machine-learned model. In a first phase, the machine-learned model is trained using non-randomized training data. Since certain types of machine-learned models, such as those involving deep learning (e.g., neural networks) require a lot of training data, this allows the bulk of the training to be devoted to training using non-randomized training data. However, since this non-randomized training data may be biased, a second training phase is then used to revise the machine-learned model based on randomized training data to remove the bias from the machine-learned model. Since this randomized training data may be less plentiful, this allows the deep learning machine-learned model to be trained to operate in an unbiased manner without the need to generate additional randomized training data.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a function call for a function that calculates an attribute associated with a machine learning model. For each argument of the function call, the system identifies a parameter type of the argument, wherein the parameter type represents a type of data used with the machine learning model. The system also obtains a value accessor for retrieving features specific to the parameter type and obtains a value represented by the argument using the value accessor. The system then calculates the attribute by applying the function to the value and uses the attribute to execute the machine learning model.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a model definition and a training configuration for a machine-learning model, wherein the training configuration includes a set of required features, a training technique, and a scoring function. Next, the system uses the model definition and the training configuration to load the machine-learning model and the set of required features into a training pipeline without requiring a user to manually identify the set of required features. The system then uses the training pipeline and the training configuration to update a set of parameters for the machine-learning model. Finally, the system stores mappings containing the updated set of parameters and the set of required features in a representation of the machine-learning model.

Abstract: Novel ent-kaurene diterpene compound and its derivatives, their preparation methods and their use. ent-Kaurene diterpene in the present invention could be used as desired intermediates for preparing asymmetric organic compounds and medicaments, and could be used as antitumor agent, anti-inflammatory agent and immune agent etc. The said ent-kaurene diterpene compound could be condensed with hydroxyl compounds to obtain various acetal derivatives, could be reacted with amine compounds to obtain various amino derivatives, and could be reacted with acyl halide or acid anhydride to obtain various acyl derivatives.

Abstract: Novel ent-kaurene diterpene compound and its derivatives, their preparation methods and their use. ent-Kaurene diterpene in the present invention could be used as desired intermediates for preparing asymmetric organic compounds and medicaments, and could be used as antitumor agent, anti-inflammatory agent and immune agent etc. The said ent-kaurene diterpene compound could be condensed with hydroxyl compounds to obtain various acetal derivatives, could be reacted with amine compounds to obtain various amino derivatives, and could be reacted with acyl halide or acid anhydride to obtain various acyl derivatives.