59 results about "Latent vector" patented technology

Disclosed herein is an evaluation predicting device including: an estimating section configured to define a plurality of first latent vectors, a plurality of second latent vectors, evaluation values, a plurality of first feature vectors, a plurality of second feature vectors, a first projection matrix, and a second projection matrix, express the first latent vectors and the second latent vectors, and perform Bayesian estimation with the first feature vectors, the second feature vectors, and a known the evaluation value as learning data, and calculate a posterior distribution of a parameter group including the first latent vectors, the second latent vectors, the first projection matrix, and the second projection matrix; and a predicting section configured to calculate a distribution of an unknown the evaluation value on a basis of the posterior distribution of the parameter group.

Systems and methods for building a latent item vector and item bias for a new item in a collaborative filtering system are disclosed. The method includes dividing incoming users into intervals with each interval having a learning phase and a selection phase. The learning phase scores each incoming user according to a best estimate latent vector and bias and saves the highest score. In the selection each incoming user is scored and a user exceeding the highest score is selected. The best estimate latent vector and bias is then updated based on the user's vector and bias, and the user's interaction with the item. The updated best estimate latent vector is then used in further intervals for learning and selecting users.

Methods and compositions to impute or predict genotype, haplotype, molecular phenotype, agronomic phenotypes, and / or coancestry are provided. Methods and compositions provided include using latent space to generate latent space representations or latent vectors that are independent of underlying genotypic or phenotypic data. The methods may include generating a universal latent space representation by encoding discrete or continuous variables derived from genotypic or phenotypic data into latent vectors through a machine learning-based encoder framework. Provided herein are universal methods of parametrically representing genotypic or phenotypic data obtained from one or more populations or sample sets to impute or predict a genotype or phenotype of interest.

A dialog generation method, apparatus, and device, and a storage medium are provided. The method includes converting each term in a Kth round of a query sentence into a first word vector, and calculating a positive latent vector and a negative latent vector of each term according to the first word vector, where K denotes a positive integer greater than or equal to 2. A content topic of the Kth round of the query sentence is obtained, and converted into a second word vector. An initial latent vector output for the Kth round of the query sentence is determined according to the second word vector, the positive latent vector of a last term in the Kth round of the query sentence, a latent vector of a last term in a (K-1)th round of a reply sentence output for a (K-1)th round of the query sentence, and an initial latent vector of the (K-1)th round of the reply sentence output for the (K-1)th round of the query sentence. A reply sentence for the Kth round of the query sentence is generated according to the positive latent vector and the negative latent vector of each term in the Kth round of the query sentence and the initial latent vector output for the Kth round of the query sentence.

The present disclosure provides a method for generating a video of a body moving in synchronization with music by applying a first artificial neural network (ANN) to a sequence of samples of an audio waveform of the music to generate a first latent vector describing the waveform and a sequence of coordinates of points of body parts of the body, by applying a first stage of a second ANN to the sequence of coordinates to generate a second latent vector describing movement of the body, by applying a second stage of the second ANN to static images of a person in a plurality of different poses to generate a third latent vector describing an appearance of the person, and by applying a third stage of the second ANN to the first latent vector, the second latent vector, and the third latent vector to generate the video.

A computer-implemented method for securing software installation through deep graph learning includes extracting a new software installation graph (SIG) corresponding to a new software installation based on installation data associated with the new software installation, using at least two node embedding models to generate a first vector representation by embedding the nodes of the new SIG and inferring any embeddings for out-of-vocabulary (OOV) words corresponding to unseen pathnames, utilizing a deep graph autoencoder to reconstruct nodes of the new SIG from latent vector representations encoded by the graph LSTM, wherein reconstruction losses resulting from a difference of a second vector representation generated by the deep graph autoencoder and the first vector representation represent anomaly scores for each node, and performing anomaly detection by comparing an overall anomaly score of the anomaly scores to a threshold of normal software installation.

A deep-learning neural network can be trained to model a probability distribution of the asset-price trends for a future time period using a training data set, which can include asset-price trends of a plurality of assets over a past time period and a latent vector sampled from a prior distribution associated with the asset-price trends of a plurality of assets. The training data set can represent a time series data. A portfolio optimization can be executed on the modeled probability distribution to estimate expected risks and returns for different portfolio diversification options.

A Kalman filter word vector learning method based on Diesel process, the method includes: training and preprocessing the corpus, generating an LDS language model system, initializing the system parameters, assuming that the process noise satisfies a normal distribution, defining the aggregation class theta t =(μ t ,∑ t ), μ t For the frequency of word t in the corpus, calculate θ t The prior distribution of Dirichlet, the posterior distribution is calculated by Kalman filter derivation and Gibbs sampling estimation, the candidate clusters are extracted by MCMC sampling algorithm, the selection probability of the candidate clusters is calculated, and the candidate with the highest probability value is selected Choose the cluster as θ t , calculate the estimated value of the minimum mean square error of the clustering, substitute the calculation result into the LDS language model, train the model through the EM algorithm, make the model parameters stable, input the preprocessed corpus into the trained LDS language model, and pass Carl The Mann filter updates the formula in one step to compute the implicit vector representation.

The embodiment of this specification provides a computer-executed method and device for evaluating interaction events, wherein the method includes first obtaining a dynamic interaction graph for reflecting the correlation of interaction events, and then using the two target nodes to be analyzed as the current root nodes , and determine the corresponding two subgraphs in the dynamic interaction graph. Input the two subgraphs into the neural network model to obtain the interaction representation vector corresponding to the interaction of the two target nodes. In the neural network model, the processing layer obtains The hidden vector of each node, the fusion layer uses the compression-transform mechanism to determine the weight estimation of the node according to the hidden vector, and update the input characteristics of the node accordingly, pass it to the next processing layer, and finally obtain the aforementioned interaction representation vector through the output layer . Therefore, based on the interaction representation vector, the interaction event of the interaction between the two target nodes can be evaluated.

A computing device for handling anomaly detection, comprises an encoder, for receiving an input image, to generate a first latent vector comprising a semantic latent vector and a visual appearance latent vector according to the input image and at least one first parameter of the encoder; and a training module, coupled to the encoder, for receiving the input image and the first latent vector, to update the at least one first parameter according to the input image and the first latent vector and a loss function.

The present invention proposes a personalized scenic spot recommendation method based on knowledge graph and user's long-term and short-term preferences, including: preprocessing the tourist's historical scenic spot sequence and performing scenic spot-coding conversion; using node2vec random walk to obtain the scenic spot sequence, using word2vec The Skip-gram model in to obtain the feature vectors of tourists and scenic spots; the feature vectors of scenic spots plus bias will be used as the input of the GRU network, and then use the GRU network to train and output the potential vector of each scenic spot; for each scenic spot Assign different weights, multiply the weight of each scenic spot with the potential vector of the scenic spot and accumulate to obtain the long-term preference of the current tourist, and then splicing the long-term preference of the current tourist with the current preference of the tourist and multiplying by the weight to obtain the final vector; Perform the dot product operation between the final vector and the current preferences of tourists to obtain the estimated rating of the scenic spots, and normalize the estimated ratings of the scenic spots to get the predicted probability of each scenic spot, and take the scenic spots corresponding to the top K scores to get the top_k scenic spot recommendations list.