Patents by Inventor Yanjun Qi
Yanjun Qi has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 9183503Abstract: Systems and methods are provided for identifying combinatorial feature interactions, including capturing statistical dependencies between categorical variables, with the statistical dependencies being stored in a computer readable storage medium. A model is selected based on the statistical dependencies using a neighborhood estimation strategy, with the neighborhood estimation strategy including generating sets of arbitrarily high-order feature interactions using at least one rule forest and optimizing one or more likelihood functions. A damped mean-field approach is applied to the model to obtain parameters of a Markov random field (MRF); a sparse high-order semi-restricted MRF is produced by adding a hidden layer to the MRF; indirect long-range dependencies between feature groups are modeled using the sparse high-order semi-restricted MRF; and a combinatorial dependency structure between variables is output.Type: GrantFiled: June 3, 2013Date of Patent: November 10, 2015Assignee: NEC Laboratories America, Inc.Inventors: Renqiang Min, Yanjun Qi
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Patent number: 8977579Abstract: Disclosed is a general learning framework for computer implementation that induces sparsity on the undirected graphical model imposed on the vector of latent factors. A latent factor model SLFA is disclosed as a matrix factorization problem with a special regularization term that encourages collaborative reconstruction. Advantageously, the model may simultaneously learn the lower-dimensional representation for data and model the pairwise relationships between latent factors explicitly. An on-line learning algorithm is disclosed to make the model amenable to large-scale learning problems. Experimental results on two synthetic data and two real-world data sets demonstrate that pairwise relationships and latent factors learned by the model provide a more structured way of exploring high-dimensional data, and the learned representations achieve the state-of-the-art classification performance.Type: GrantFiled: October 11, 2012Date of Patent: March 10, 2015Assignee: NEC Laboratories America, Inc.Inventors: Yunlong He, Yanjun Qi, Koray Kavukcuoglu
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Patent number: 8892488Abstract: Methods and systems for document classification include embedding n-grams from an input text in a latent space, embedding the input text in the latent space based on the embedded n-grams and weighting said n-grams according to spatial evidence of the respective n-grams in the input text, classifying the document along one or more axes, and adjusting weights used to weight the n-grams based on the output of the classifying step.Type: GrantFiled: May 30, 2012Date of Patent: November 18, 2014Assignee: NEC Laboratories America, Inc.Inventors: Yanjun Qi, Bing Bai
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Patent number: 8874432Abstract: Systems and methods are disclosed to perform relation extraction in text by applying a convolution strategy to determine a kernel between sentences; applying one or more semi-supervised strategies to the kernel to encode syntactic and semantic information to recover a relational pattern of interest; and applying a classifier to the kernel to identify the relational pattern of interest in the text in response to a query.Type: GrantFiled: April 3, 2011Date of Patent: October 28, 2014Assignee: NEC Laboratories America, Inc.Inventors: Yanjun Qi, Bing Bai, Xia Ning, Pavel Kuksa
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Publication number: 20140309122Abstract: Systems and methods are disclosed for Knowledge-Driven Sparse Learning to Identify Interpretable High-Order Feature Interactions. This is done by generating one or more functional groups from gene features and gene and protein interaction grouping; selecting informative genes and functional interactions that exhibit differential patterns for the target disease and to generate a reduced feature space; and searching exhaustively on the reduced feature space by examining all possible pairs of interacting features (and possibly higher-order feature interactions) to identify combination of markers and complex patterns of feature interactions that are informative about the phenotypes in a sparse learning framework to select informative interactions and genes.Type: ApplicationFiled: April 3, 2014Publication date: October 16, 2014Applicant: NEC Laboratories America, Inc.Inventors: Renqiang Min, Yanjun Qi, Salim Akhter Chowdhury
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Patent number: 8738547Abstract: Systems and methods are disclosed to perform preference learning on a set of documents includes receiving raw input features from the set of documents stored on a data storage device; generating polynomial combinations from the raw input features; generating one or more parameters; applying the parameters to one or more classifiers to generate outputs; determining a loss function and parameter gradients and updating parameters determining one or more sparse regularizing terms and updating the parameters; and expressing that one document is preferred over another in a search query and retrieving one or more documents responsive to the search query.Type: GrantFiled: April 8, 2011Date of Patent: May 27, 2014Assignee: NEC Laboratories America, Inc.Inventors: Xi Chen, Yanjun Qi, Bing Bai
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Patent number: 8612369Abstract: Systems and methods are disclosed to perform preference learning on a set of documents includes receiving raw input features from the set of documents stored on a data storage device; generating polynomial combinations from the raw input features; generating one or more parameters; applying the parameters to one or more classifiers to generate outputs; determining a loss function and parameter gradients and updating parameters determining one or more sparse regularizing terms and updating the parameters; and expressing that one document is preferred over another in a search query and retrieving one or more documents responsive to the search query.Type: GrantFiled: April 8, 2011Date of Patent: December 17, 2013Assignee: NEC Laboratories Amercia, Inc.Inventors: Xi Chen, Yanjun Qi, Bing Bai
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Publication number: 20130325786Abstract: Systems and methods are provided for identifying combinatorial feature interactions, including capturing statistical dependencies between categorical variables, with the statistical dependencies being stored in a computer readable storage medium. A model is selected based on the statistical dependencies using a neighborhood estimation strategy, with the neighborhood estimation strategy including generating sets of arbitrarily high-order feature interactions using at least one rule forest and optimizing one or more likelihood functions. A damped mean-field approach is applied to the model to obtain parameters of a Markov random field (MRF); a sparse high-order semi-restricted MRF is produced by adding a hidden layer to the MRF; indirect long-range dependencies between feature groups are modeled using the sparse high-order semi-restricted MRF; and a combinatorial dependency structure between variables is output.Type: ApplicationFiled: June 3, 2013Publication date: December 5, 2013Inventors: Renqiang Min, Yanjun Qi
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Patent number: 8521662Abstract: Systems and methods are disclosed to perform preference learning on a set of documents includes receiving raw input features from the set of documents stored on a data storage device; generating polynomial combinations from the raw input features; generating one or more parameters; applying the parameters to one or more classifiers to generate outputs; determining a loss function and parameter gradients and updating parameters determining one or more sparse regularizing terms and updating the parameters; and expressing that one document is preferred over another in a search query and retrieving one or more documents responsive to the search query.Type: GrantFiled: April 3, 2011Date of Patent: August 27, 2013Assignee: NEC Laboratories America, Inc.Inventors: Xi Chen, Yanjun Qi, Bing Bai
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Publication number: 20120323825Abstract: Systems and methods are disclosed to perform preference learning on a set of documents includes receiving raw input features from the set of documents stored on a data storage device; generating polynomial combinations from the raw input features; generating one or more parameters; applying the parameters to one or more classifiers to generate outputs; determining a loss function and parameter gradients and updating parameters determining one or more sparse regularizing terms and updating the parameters; and expressing that one document is preferred over another in a search query and retrieving one or more documents responsive to the search query.Type: ApplicationFiled: April 3, 2011Publication date: December 20, 2012Applicant: NEC LABORATORIES AMERICA, INC.Inventors: Xi Chen, Yanjun Qi, Bing Bai
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Publication number: 20120310627Abstract: Methods and systems for document classification include embedding n-grams from an input text in a latent space, embedding the input text in the latent space based on the embedded n-grams and weighting said n-grams according to spatial evidence of the respective n-grams in the input text, classifying the document along one or more axes, and adjusting weights used to weight the n-grams based on the output of the classifying step.Type: ApplicationFiled: May 30, 2012Publication date: December 6, 2012Applicant: NEC Laboratories America, Inc.Inventors: YANJUN QI, BING BAI
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Publication number: 20120253792Abstract: A method for sentiment classification of a text document using high-order n-grams utilizes a multilevel embedding strategy to project n-grams into a low-dimensional latent semantic space where the projection parameters are trained in a supervised fashion together with the sentiment classification task. Using, for example, a deep convolutional neural network, the semantic embedding of n-grams, the bag-of-occurrence representation of text from n-grams, and the classification function from each review to the sentiment class are learned jointly in one unified discriminative framework.Type: ApplicationFiled: March 20, 2012Publication date: October 4, 2012Applicant: NEC LABORATORIES AMERICA, INC.Inventors: Dmitriy Bespalov, Bing Bai, Yanjun Qi
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Publication number: 20120191632Abstract: Systems and methods are disclosed to perform preference learning on a set of documents includes receiving raw input features from the set of documents stored on a data storage device; generating polynomial combinations from the raw input features; generating one or more parameters; applying the parameters to one or more classifiers to generate outputs; determining a loss function and parameter gradients and updating parameters determining one or more sparse regularizing terms and updating the parameters; and expressing that one document is preferred over another in a search query and retrieving one or more documents responsive to the search query.Type: ApplicationFiled: April 8, 2011Publication date: July 26, 2012Applicant: NEC LABORATORIES AMERICA, INC.Inventors: Xi Chen, Yanjun Qi, Bing Bai
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Publication number: 20110270604Abstract: Systems and methods are disclosed to perform relation extraction in text by applying a convolution strategy to determine a kernel between sentences; applying one or more semi-supervised strategies to the kernel to encode syntactic and semantic information to recover a relational pattern of interest; and applying a classifier to the kernel to identify the relational pattern of interest in the text in response to a query.Type: ApplicationFiled: April 3, 2011Publication date: November 3, 2011Applicant: NEC LABORATORIES AMERICA, INC.Inventors: Yanjun Qi, Xia Ning, Pavel Kuksa, Bing Bai