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    LATENT COLLABORATIVE RETRIEVAL
    1.
    发明申请
    LATENT COLLABORATIVE RETRIEVAL 审中-公开
    专利合作研究

    公开(公告)号：US20130325846A1

    公开(公告)日：2013-12-05

    申请号：US13486696

    申请日：2012-06-01

    申请人： JASON WESTON ,  Ron Weiss ,  Adam Berenzweig ,  Chong Wang

    发明人： JASON WESTON ,  Ron Weiss ,  Adam Berenzweig ,  Chong Wang

    IPC分类号： G06F17/30

    CPC分类号： G06F16/9535

    摘要： A method, computer program product, and computer system for latent collaborative retrieval are described. A first mathematical representation of a query received from a user is generated. A second mathematical representation of a user profile is generated. A plurality of mathematical representations associated with a plurality of items is accessed. The first mathematical representation, the second mathematical representation, and the plurality of mathematical representations are transformed to have a uniform length. A first results subset of items is generated, based upon, at least in part, a first similarity measurement of the first mathematical representation and the plurality of mathematical representations. A second result subset of items is generated based upon, at least in part, a second similarity measurement of the second mathematical representation and the plurality of mathematical representations. A result set of items is generated based upon, at least in part, the first and second result subsets.

    摘要翻译： 描述了潜在协同检索的方法，计算机程序产品和计算机系统。 生成从用户接收的查询的第一数学表示。 生成用户简档的第二数学表示。 访问与多个项目相关联的多个数学表示。 第一数学表示，第二数学表示和多个数学表示被转换为具有均匀的长度。 基于至少部分地基于第一数学表示和多个数学表示的第一相似度测量来生成项目的第一结果子集。 至少部分地基于第二数学表示和多个数学表示的第二相似性测量来生成项目的第二结果子集。 至少部分地基于第一和第二结果子集产生结果集项。

    Deep neural networks and methods for using same
    2.
    发明授权
    Deep neural networks and methods for using same 有权
    深层神经网络及其使用方法

    公开(公告)号：US08504361B2

    公开(公告)日：2013-08-06

    申请号：US12367788

    申请日：2009-02-09

    申请人： Ronan Collobert ,  Jason Weston

    发明人： Ronan Collobert ,  Jason Weston

    IPC分类号： G10L15/16

    CPC分类号： G06F17/277

    摘要： A method and system for labeling a selected word of a sentence using a deep neural network includes, in one exemplary embodiment, determining an index term corresponding to each feature of the word, transforming the index term or terms of the word into a vector, and predicting a label for the word using the vector. The method and system, in another exemplary embodiment, includes determining, for each word in the sentence, an index term corresponding to each feature of the word, transforming the index term or terms of each word in the sentence into a vector, applying a convolution operation to the vector of the selected word and at least one of the vectors of the other words in the sentence, to transform the vectors into a matrix of vectors, each of the vectors in the matrix including a plurality of row values, constructing a single vector from the vectors in the matrix, and predicting a label for the selected word using the single vector.

    摘要翻译： 在一个示例性实施例中，用于使用深层神经网络标记句子的选定单词的方法和系统包括：确定对应于单词的每个特征的索引项，将该词的索引项或项变换为向量，以及 使用向量预测单词的标签。 在另一示例性实施例中，该方法和系统包括为每个词语确定与单词的每个特征相对应的索引项，将该词中的每个单词的索引项或项变换为向量，应用卷积 对所选择的单词的向量和句子中的其他单词的向量中的至少一个进行操作，将向量变换为向量矩阵，矩阵中的每个矢量包括多个行值，构成单个 向量，并使用单个向量来预测所选择的单词的标签。

    RECURSIVE FEATURE ELIMINATION METHOD USING SUPPORT VECTOR MACHINES
    4.
    发明申请
    RECURSIVE FEATURE ELIMINATION METHOD USING SUPPORT VECTOR MACHINES 审中-公开
    使用支持向量机的回归特征消除方法

    公开(公告)号：US20110106735A1

    公开(公告)日：2011-05-05

    申请号：US12944197

    申请日：2010-11-11

    申请人： Jason Weston ,  André Elisseeff ,  Bernhard Schölkopf ,  Fernando Perez-Cruz ,  Isabelle Guyon

    发明人： Jason Weston ,  André Elisseeff ,  Bernhard Schölkopf ,  Fernando Perez-Cruz ,  Isabelle Guyon

    IPC分类号： G06F15/18

    CPC分类号： G06K9/6231 ,  G06N20/00 ,  G06N20/10 ,  G16B25/00 ,  G16B40/00

    摘要： Identification of a determinative subset of features from within a group of features is performed by training a support vector machine using training samples with class labels to determine a value of each feature, where features are removed based on their the value. One or more features having the smallest values are removed and an updated kernel matrix is generated using the remaining features. The process is repeated until a predetermined number of features remain which are capable of accurately separating the data into different classes. In some embodiments, features are eliminated by a ranking criterion based on a Lagrange multiplier corresponding to each training sample.

    摘要翻译： 通过使用具有类标签的训练样本来训练支持向量机来确定特征组中的特征的确定性子集来确定每个特征的值，其中基于其特征被去除。 删除具有最小值的一个或多个特征，并且使用其余特征生成更新的内核矩阵。 重复该过程，直到保持能够将数据精确地分离成不同类别的预定数量的特征。 在一些实施例中，通过基于对应于每个训练样本的拉格朗日乘数的排序标准来消除特征。

    SUPERVISED SEMANTIC INDEXING AND ITS EXTENSIONS
    5.
    发明申请
    SUPERVISED SEMANTIC INDEXING AND ITS EXTENSIONS 有权
    监督语义索引及其扩展

    公开(公告)号：US20100179933A1

    公开(公告)日：2010-07-15

    申请号：US12562802

    申请日：2009-09-18

    申请人： BING BAI ,  Jason Weston ,  Ronan Collobert ,  David Grangier

    发明人： BING BAI ,  Jason Weston ,  Ronan Collobert ,  David Grangier

    IPC分类号： G06F17/30 ,  G06F15/18

    CPC分类号： G06F17/30663 ,  G06F17/30616

    摘要： A system and method for determining a similarity between a document and a query includes building a weight vector for each of a plurality of documents in a corpus of documents stored in memory and building a weight vector for a query input into a document retrieval system. A weight matrix is generated which distinguishes between relevant documents and lower ranked documents by comparing document/query tuples using a gradient step approach. A similarity score is determined between weight vectors of the query and documents in a corpus by determining a product of a document weight vector, a query weight vector and the weight matrix.

    摘要翻译： 用于确定文档和查询之间的相似度的系统和方法包括为存储在存储器中的文档的语料库中的多个文档中的每个文档建立权重向量，并且建立用于向文档检索系统输入的查询的加权向量。 生成权重矩阵，通过使用梯度步骤方法比较文档/查询元组来区分相关文档和较低排名的文档。 通过确定文档权重向量，查询权重向量和权重矩阵的乘积，在查询的权重向量和语料库中的文档之间确定相似性得分。

    Deep Neural Networks and Methods for Using Same
    6.
    发明申请
    Deep Neural Networks and Methods for Using Same 有权
    深层神经网络及其使用方法

    公开(公告)号：US20090210218A1

    公开(公告)日：2009-08-20

    申请号：US12367788

    申请日：2009-02-09

    申请人： Ronan Collobert ,  Jason Weston

    发明人： Ronan Collobert ,  Jason Weston

    IPC分类号： G06F17/27

    CPC分类号： G06F17/277

    摘要： A method and system for labeling a selected word of a sentence using a deep neural network includes, in one exemplary embodiment, determining an index term corresponding to each feature of the word, transforming the index term or terms of the word into a vector, and predicting a label for the word using the vector. The method and system, in another exemplary embodiment, includes determining, for each word in the sentence, an index term corresponding to each feature of the word, transforming the index term or terms of each word in the sentence into a vector, applying a convolution operation to the vector of the selected word and at least one of the vectors of the other words in the sentence, to transform the vectors into a matrix of vectors, each of the vectors in the matrix including a plurality of row values, constructing a single vector from the vectors in the matrix, and predicting a label for the selected word using the single vector.

    摘要翻译： 在一个示例性实施例中，用于使用深层神经网络标记句子的选定单词的方法和系统包括：确定对应于单词的每个特征的索引项，将该词的索引项或项变换为向量，以及 使用向量预测单词的标签。 在另一示例性实施例中，该方法和系统包括为每个词语确定与单词的每个特征相对应的索引项，将该词中的每个单词的索引项或项变换为向量，应用卷积 对所选择的单词的向量和句子中的其他单词的向量中的至少一个进行操作，将向量变换为向量矩阵，矩阵中的每个矢量包括多个行值，构成单个 向量，并使用单个向量来预测所选择的单词的标签。

    FAST SEMANTIC EXTRACTION USING A NEURAL NETWORK ARCHITECTURE
    7.
    发明申请
    FAST SEMANTIC EXTRACTION USING A NEURAL NETWORK ARCHITECTURE 有权
    使用神经网络架构进行快速语义提取

    公开(公告)号：US20080221878A1

    公开(公告)日：2008-09-11

    申请号：US12039965

    申请日：2008-02-29

    申请人： Ronan Collobert ,  Jason Weston

    发明人： Ronan Collobert ,  Jason Weston

    IPC分类号： G10L15/16

    CPC分类号： G06F17/2785

    摘要： A system and method for semantic extraction using a neural network architecture includes indexing each word in an input sentence into a dictionary and using these indices to map each word to a d-dimensional vector (the features of which are learned). Together with this, position information for a word of interest (the word to labeled) and a verb of interest (the verb that the semantic role is being predicted for) with respect to a given word are also used. These positions are integrated by employing a linear layer that is adapted to the input sentence. Several linear transformations and squashing functions are then applied to output class probabilities for semantic role labels. All the weights for the whole architecture are trained by backpropagation.

    摘要翻译： 使用神经网络架构的语义提取的系统和方法包括将输入语句中的每个单词索引到词典中，并且使用这些索引将每个单词映射到d维向量（其特征被学习）。 与此同时，还使用了一个关于一个给定单词的感兴趣的词的位置信息（被标记的词）和一个感兴趣的动词（语义角色被预测的动词）。 通过采用适合于输入句子的线性层来集成这些位置。 然后将多个线性变换和压缩函数应用于语义角色标签的输出类概率。 整个建筑的所有重量都通过反向传播进行训练。

    Joint embedding for item association
    9.
    发明授权
    Joint embedding for item association 有权
    联合嵌入项目关联

    公开(公告)号：US09110922B2

    公开(公告)日：2015-08-18

    申请号：US13019221

    申请日：2011-02-01

    申请人： Samy Bengio ,  Jason Weston

    发明人： Samy Bengio ,  Jason Weston

    IPC分类号： G06F7/00 ,  G06F17/30

    CPC分类号： G06F17/30244 ,  G06F17/30879

    摘要： Methods and systems to associate semantically-related items of a plurality of item types using a joint embedding space are disclosed. The disclosed methods and systems are scalable to large, web-scale training data sets. According to an embodiment, a method for associating semantically-related items of a plurality of item types includes embedding training items of a plurality of item types in a joint embedding space configured in a memory coupled to at least one processor, learning one or more mappings into the joint embedding space for each of the item types to create a trained joint embedding space and one or more learned mappings, and associating one or more embedded training items with a first item based upon a distance in the trained joint embedding space from the first item to each said associated embedded training items. Exemplary item types that may be embedded in the joint embedding space include images, annotations, audio and video.

    摘要翻译： 公开了使用联合嵌入空间来关联多个项目类型的语义相关项目的方法和系统。 所公开的方法和系统可扩展到大型的web规模的训练数据集。 根据实施例，一种用于关联多个项目类型的语义相关项目的方法包括：将多个项目类型的训练项目嵌入到配置在耦合到至少一个处理器的存储器中的联合嵌入空间中，学习一个或多个映射 进入用于每个项目类型的联合嵌入空间以创建经训练的联合嵌入空间和一个或多个学习的映射，以及基于训练的关节嵌入空间中的距离与第一项目相关联的一个或多个嵌入式训练项目与第一项目 项目对每个说相关的嵌入式培训项目。 可以嵌入在联合嵌入空间中的示例性项目类型包括图像，注释，音频和视频。

    Supervised semantic indexing and its extensions
    10.
    发明授权
    Supervised semantic indexing and its extensions 有权
    监督语义索引及其扩展

    公开(公告)号：US08341095B2

    公开(公告)日：2012-12-25

    申请号：US12562802

    申请日：2009-09-18

    申请人： Bing Bai ,  Jason Weston ,  Ronan Collobert ,  David Grangier

    发明人： Bing Bai ,  Jason Weston ,  Ronan Collobert ,  David Grangier

    IPC分类号： G06F15/18 ,  G06F7/00 ,  G06F3/00

    CPC分类号： G06F17/30663 ,  G06F17/30616

    摘要： A system and method for determining a similarity between a document and a query includes building a weight vector for each of a plurality of documents in a corpus of documents stored in memory and building a weight vector for a query input into a document retrieval system. A weight matrix is generated which distinguishes between relevant documents and lower ranked documents by comparing document/query tuples using a gradient step approach. A similarity score is determined between weight vectors of the query and documents in a corpus by determining a product of a document weight vector, a query weight vector and the weight matrix.

    摘要翻译： 用于确定文档和查询之间的相似度的系统和方法包括为存储在存储器中的文档的语料库中的多个文档中的每个文档建立权重向量，并且建立用于向文档检索系统输入的查询的加权向量。 生成权重矩阵，通过使用梯度步骤方法比较文档/查询元组来区分相关文档和较低排名的文档。 通过确定文档权重向量，查询权重向量和权重矩阵的乘积，在查询的权重向量和语料库中的文档之间确定相似性得分。