Abstract: The embodiment of the invention provides method, apparatus, device and a storage medium for training and optimizing an analysis model. The method of optimizing the analysis model includes: fine-tuning an analysis model with a first set of values regarding a first property of a target material to determine a second set of values regarding a second property of the target material; determining an association between the first property and the second property of the target material based on a first set of values and a second set of values; determining a target value of the target material regarding the first property with the association based on a reference value of the target material regarding the second property, the reference value being determined based on an experiment on target material; and optimizing the analysis model with the target value of the target material regarding the first property.

Abstract: According to embodiments of the present disclosure, there are provided a method, device, medium, and product for state prediction. The method includes: obtaining a neural network, the neural network being trained to determine a state change of a physical system over time, training data of the neural network indicating states of a plurality of physical systems at a plurality of times; obtaining state data corresponding to a state of a target physical system at a first time; determining respective unit feature representations of the physical units in the target physical system based at least on target values of material properties of the physical units; and determining a state of the target physical system at a second time based on the state data by inputting at least the unit feature representations to the neural network. Through the above solution, generalization capability of the neural network can be significantly improved.

Abstract: According to implementations of the present disclosure, a method, apparatus, device and medium for managing molecular prediction is provided. In the method, an upstream model is obtained from a portion of network layers in a pretrained model, the pretrained model describing an association between a molecular structure and molecular energy. A downstream model is determined based on a molecular prediction purpose, and an output layer of the downstream model is determined based on the molecular prediction purpose. A molecular prediction model is generated based on the upstream model and the downstream model, the molecular prediction model describing an association between a molecular structure and a molecular prediction purpose associated with the molecular structure. Since the upstream model may have extensive knowledge related to molecules, the amount of training data required to train the molecular prediction model that is generated based on the upstream model and the downstream model may be reduced.

Abstract: The present disclosure relates to a method and a device for data protection, a readable medium and an electronic apparatus, and the method comprises: acquiring a target identification information union set, wherein the target identification information union set comprises target encryption identification information of a first party of a joint training model and target encryption identification information of a second party of the joint training model, the target encryption identification information in the target identification information union set being obtained by encrypting according to a secret key of the first party and a secret key of the second party; and determining, according to the target identification information union set, a target sample data set for training the joint training model. Therefore, an identification information intersection of the first party and the second party does not need to be determined in advance as in the related technology.

Abstract: The present disclosure relates to a data protection method, a training method and apparatus for a network structure, a medium, and a device. The data protection method includes: obtaining original feature information of a target batch of reference samples for a passive party of a joint training model; and processing the original feature information by means of a target feature processing network structure to obtain target feature information corresponding to the original feature information. A neural network structure is trained by at least aiming at minimizing a coupling degree of between original training feature information and target training feature information of training samples for the passive party to obtain the target feature processing network structure. The target training feature information is feature information corresponding to the original training feature information that is outputted from the neural network structure using the original training feature information as an input.

Abstract: The present disclosure relates to a data protection method, apparatus, medium and electronic device. The method comprises: obtaining a specified batch of reference samples of an active participant of a joint training model; determining generation gradient information of the first reference sample; determining target gradient information sent to the passive participant according to the generation gradient information, and sending the target gradient information to the passive participant, to update, by the passive participant, parameters of the joint training model according to the target gradient information. Through the above solution, the influence of the generated data on the training process and model performance of the joint training model is avoided as much as possible, and the privacy and security of data are improved.

Abstract: The present disclosure relates to a data protection method, an apparatus, a medium and a device. The method includes: acquiring gradient association information respectively corresponding to reference samples of a target batch of an active party of a joint training model; according to the proportion occupied respectively by reference samples of positive examples and reference samples of negative examples in all reference samples of the target batch, determining a constraint condition of the data noise to be added; determining information of said data noise according to the gradient association information and the constraint condition corresponding to the reference samples; correcting, according to the information of said data noise, an initial gradient transmission value corresponding to each reference sample, so as to obtain target gradient transmission information; and sending the target gradient transmission information to a passive party of the joint training model.

Abstract: Disclosed in the embodiments of the present invention are a method and an apparatus for training an item coding model. The method comprises: acquiring an initial item coding model and a training sample set; using sample user information of training samples in the training sample set as the input for the initial item coding model to obtain the probability of sample item coding information corresponding to the inputted sample user information; adjusting the structural parameters of the initial item coding model to train an item coding model, the item coding model being used for characterizing the correspondence between inputted sample user information and sample item coding information and the correspondence between sample item information and sample item coding information. The present embodiment can use the trained item coding model to implement item recommendation and can use the item coding information as an index to increase retrieval efficiency.

Abstract: Disclosed are a data protection method and apparatus, and a server and a medium. A particular embodiment of the method comprises: acquiring gradient associated information, which respectively corresponds to a target sample that belongs to a binary classification sample set with unbalanced distribution and a reference sample that belongs to the same batch as the target sample; generating information of data noise to be added; according to the information of said data noise, correcting an initial gradient transfer value corresponding to the target sample, such that corrected gradient transfer information corresponding to samples in the sample set that belong to different types is consistent; and sending the gradient transfer information to a passive party of a joint training model. By means of the embodiment, there is no significant difference between corrected gradient transfer information corresponding to positive and negative samples, thereby effectively protecting the security of data.

Abstract: Disclosed are a data protection method and apparatus, and a server and a medium. A particular embodiment of the method comprises: acquiring gradient associated information, which respectively corresponds to a target sample that belongs to a binary classification sample set with unbalanced distribution and a reference sample that belongs to the same batch as the target sample; generating information of data noise to be added; according to the information of said data noise, correcting an initial gradient transfer value corresponding to the target sample, such that corrected gradient transfer information corresponding to samples in the sample set that belong to different types is consistent; and sending the gradient transfer information to a passive party of a joint training model. By means of the embodiment, there is no significant difference between corrected gradient transfer information corresponding to positive and negative samples, thereby effectively protecting the security of data.

Abstract: Disclosed in the embodiments of the present invention are a method and an apparatus for training an item coding model. The method comprises: acquiring an initial item coding model and a training sample set; using sample user information of training samples in the training sample set as the input for the initial item coding model to obtain the probability of sample item coding information corresponding to the inputted sample user information; adjusting the structural parameters of the initial item coding model to train an item coding model, the item coding model being used for characterizing the correspondence between inputted sample user information and sample item coding information and the correspondence between sample item information and sample item coding information. The present embodiment can use the trained item coding model to implement item recommendation and can use the item coding information as an index to increase retrieval efficiency.