32 results about "Probabilistic generative model" patented technology

A system and method facilitating object tracking is provided. The system includes an audio model that receives at least two audio input signals and a video model that receives a video input. The audio model and the video model employ probabilistic generative models which are combined to facilitate object tracking. Expectation maximization can be employed to modify trainable parameters of the audio model and the video model.

One embodiment of the present invention provides a system that learns a generative model for textual documents. During operation, the system receives a current model, which contains terminal nodes representing random variables for words and cluster nodes representing clusters of conceptually related words. Within the current model, nodes are coupled together by weighted links, so that if a cluster node in the probabilistic model fires, a weighted link from the cluster node to another node causes the other node to fire with a probability proportionate to the link weight. The system also receives a set of training documents, wherein each training document contains a set of words. Next, the system applies the set of training documents to the current model to produce a new model.

A system and method facilitating speech detection and / or enhancement utilizing audio / video fusion is provided. The present invention fuses audio and video in a probabilistic generative model that implements cross-model, self-supervised learning, enabling rapid adaptation to audio visual data. The system can learn to detect and enhance speech in noise given only a short (e.g., 30 second) sequence of audio-visual data. In addition, it automatically learns to track the lips as they move around in the video.

A method may include receiving, at one or more processors, a current model. The current model may include a group of nodes representing words, at least one cluster of nodes representing related words, and a group of links. Each link may connect two nodes of the group of nodes. Each link may include a corresponding weight. The method may further include applying, by one or more processors, a set of training documents to the model to produce new weights for the group of links to create a new model; and making, by one or more processors, the new model the current model.

The invention discloses a POI recommendation method based on a heterogeneous network, relates to the technical field of POI recommendation, and establishes a new comment statement probability generation model. The model can handle cross-site heterogeneous data, and simultaneously considers geographical area division, The impact of user community division, comment content, and user interaction behavior on POI prediction results solves the problem of data sparseness and low user trust in the recommendation process, and can significantly improve the accuracy of POI recommendation.

The utility model relates to a DBN (Deep Belief Network) based ADHD (Attention Deficit Hyperactivity Disorder) discriminatory analysis method. The ADHD discriminatory analysis method comprises the following steps: step 1, pre-processing; step 2, characteristic extracting and classifying: depending on the DBN that is formed by stacking RBMs (Restricted Boltzmann Machines), classified and reversely adjusted in a layer-by-layer manner through softmax finally. The targets of the RBMs in layer-by-layer training are to maximize the likelihood function of the probability function, to introduce in the comparison divergence, and to update the weight function, so that the hidden layer becomes the approximate representation of the visible layer, the hidden layer of the first layer serves as the visible layer of the second layer, by parity of reasoning, the RBM layers of the DBN are obtained, and the last hidden layer is adopted as the input of the softmax to obtain the corresponding output, namely, the classification. The adopted DBN is a probability generative model, is formed by stacking the multiple RBMs with the hidden layers and the visible layers, simulates the layer-by-layer abstract characteristic process when the human brain processes signals, and abstracts the equivalent characteristic expression of the original signals to apply in the field of ADHD classification.

The invention discloses a depth generation confrontation method for underwater acoustic signal denoising, and belongs to the technical field of underwater acoustic signal denoising. In this method, the original underwater acoustic signal is first sampled and feature extracted, and then the extracted signal is sent to a Gauss-restricted Boltzmann machine for semi-supervised pre-training of the probability generation model; finally, a deep generation confrontation model is constructed, and the The data generated in the probability generation model and the real label data flow are sent to the Bernoulli restricted Boltzmann machine confrontation model for supervised training. Aiming at the feature extraction characteristics of the underwater acoustic signal, the present invention introduces a generative confrontation model into the restricted Boltzmann probability model, effectively eliminating the restricted Boltzmann machine during the training process caused by the complex signal carried by the underwater acoustic signal. The strong dependence and overfitting problems of the training model make the training model more self-applicable.

The invention relates to a friend recommendation method based on friend relationship spread in a social network, and belongs to the technical field of computer data mining. The method includes the steps that a potential friend relationship spread network is created for each self node; in each time period, a social behavior probability generative model is built, wherein social behaviors are perceived by interests, candidate middlemen and candidate interest fields are updated in an iterative mode, and the side-to-side weight in the potential friend relationship spread network is set according to the iterative result; as for each self node, the probability of choosing every friend to be the middleman is calculated on the partial layer of the potential friend relationship spread network in a random walking mode, the probability of choosing every node to be new friends is calculated on the whole layer of the potential friend relationship spread network in a random walking mode, and then the new friends with the largest probability are added in a recommended friend list. By constructing the potential friend relationship spread network, reasons and the structure of the social network are deeply analyzed from the angle of user behaviors, and then accurate friend recommendation services are provided.