Abstract: The relevance of advertisements to a user's interests is improved. In one implementation, the content of a web page is analyzed to determine a list of one or more topics associated with that web page. An advertisement is considered to be relevant to that web page if it is associated with keywords belonging to the list of one or more topics. One or more of these relevant advertisements may be provided for rendering in conjunction with the web page or related web pages.

Abstract: Targeting information (also referred to as ad “serving constraints”) or candidate targeting information for an advertisement is identified. Targeting information may be identified by extracting topics or concepts from, and/or generating topics or concepts based on, ad information, such as information from a Web page to which an ad is linked (or some other Web page of interest to the ad or advertiser). The topics or concepts may be relevant queries associated with the Web page of interest, clusters, etc.

Abstract: A data compression system includes: (a) a data compression module that receives a sequence of input vectors and that provides a sequence of compressed vectors; (b) a data decompression module that receives the compressed vectors to provide a sequence of output vectors; and (c) a parameter update module that receives the sequence of input vectors and the sequence of output vectors, and which learns the data compression module and data decompression module based on evaluating a loss function of the input vectors, the output vectors, and the parameters controlling the compression module and the decompression module. Each input vector and its corresponding output vector may represent digitized time-domain signals (e.g., speech, audio or video signals) over a predetermined time period. The loss function may be evaluated for each of a sequence of predetermined time periods.

Abstract: Keyword suggestions that are category-aware (and field-proven) may be used to help advertisers better target the serving of their ads, and may reduce unused ad spot inventory. The advertiser can enter ad information, such as a creative, a landing Webpage, other keywords, etc. for example. A keyword facility may use this entered ad information as seed information to infer one or more categories. It may then request that the advertiser confirm or deny some basic feedback information (e.g., categories, Webpage information, etc.). For example, an advertiser may be provided with candidate categories and may be asked to confirm (e.g., using checkboxes) which of the categories are relevant to their ad. Keywords may be determined using at least the categories. The determined keywords may be provided to the advertiser as suggested keywords, or may automatically populate ad serving constraint information as targeting keywords.

Abstract: Keyword suggestions that are category-aware (and field-proven) may be used to help advertisers better target the serving of their ads, and may reduce unused ad spot inventory. The advertiser can enter ad information, such as a creative, a landing Webpage, other keywords, etc. for example. A keyword facility may use this entered ad information as seed information to infer one or more categories. It may then request that the advertiser confirm or deny some basic feedback information (e.g., categories, Webpage information, etc.). For example, an advertiser may be provided with candidate categories and may be asked to confirm (e.g., using checkboxes) which of the categories are relevant to their ad. Keywords may be determined using at least the categories. The determined keywords may be provided to the advertiser as suggested keywords, or may automatically populate ad serving constraint information as targeting keywords.

Abstract: Targeting information (also referred to as ad “serving constraints”) or candidate targeting information for an advertisement is identified. Targeting information may be identified by extracting topics or concepts from, and/or generating topics or concepts based on, ad information, such as information from a Web page to which an ad is linked (or some other Web page of interest to the ad or advertiser). The topics or concepts may be relevant queries associated with the Web page of interest, clusters, etc.

Abstract: A method which does not rely on explicit inverted indices is provided to search for documents in a corpus of documents responsive to a textual search query. The method includes (a) selecting a program that is customized by setting values for a plurality of parameters, the program structured to receive the textual search query as input and to provide as output values indicating the relevance of the documents in the corpus to the search query; (b) training the program using a machine learning technique; and (c) applying the trained program to the textual search query. The program may be based on a structure that is developed based on a genetic programming technique.

Abstract: The relevance of advertisements to a user's interests is improved. In one implementation, the content of a web page is analyzed to determine a list of one or more topics associated with that web page. An advertisement is considered to be relevant to that web page if it is associated with keywords belonging to the list of one or more topics. One or more of these relevant advertisements may be provided for rendering in conjunction with the web page or related web pages.

Abstract: The relevance of advertisements to a user's interests is improved. In one implementation, the content of a web page is analyzed to determine a list of one or more topics associated with that web page. An advertisement is considered to be relevant to that web page if it is associated with keywords belonging to the list of one or more topics. One or more of these relevant advertisements may be provided for rendering in conjunction with the web page or related web pages.

Abstract: Targeting information (also referred to as ad “serving constraints”) or candidate targeting information for an advertisement is identified. Targeting information may be identified by extracting topics or concepts from, and/or generating topics or concepts based on, ad information, such as information from a Web page to which an ad is linked (or some other Web page of interest to the ad or advertiser). The topics or concepts may be relevant queries associated with the Web page of interest, clusters, etc.

Abstract: A data compression system includes: (a) a data compression module that receives a sequence of input vectors and that provides a sequence of compressed vectors; (b) a data decompression module that receives the compressed vectors to provide a sequence of output vectors; and (c) a parameter update module that receives the sequence of input vectors and the sequence of output vectors, and which learns the data compression module and data decompression module based on evaluating a loss function of the input vectors, the output vectors, and the parameters controlling the compression module and the decompression module. Each input vector and its corresponding output vector may represent digitized time-domain signals (e.g., speech, audio or video signals) over a predetermined time period. The loss function may be evaluated for each of a sequence of predetermined time periods.

Abstract: The relevance of advertisements to a user's interests is improved. In one implementation, the content of a web page is analyzed to determine a list of one or more topics associated with that web page. An advertisement is considered to be relevant to that web page if it is associated with keywords belonging to the list of one or more topics. One or more of these relevant advertisements may be provided for rendering in conjunction with the web page or related web pages.

Abstract: The relevance of advertisements to a user's interests is improved. In one implementation, the content of a web page is analyzed to determine a list of one or more topics associated with that web page. An advertisement is considered to be relevant to that web page if it is associated with keywords belonging to the list of one or more topics. One or more of these relevant advertisements may be provided for rendering in conjunction with the web page or related web pages.

Abstract: Keyword suggestions that are category-aware (and field-proven) may be used to help advertisers better target the serving of their ads, and may reduce unused ad spot inventory. The advertiser can enter ad information, such as a creative, a landing Webpage, other keywords, etc. for example. A keyword facility may use this entered ad information as seed information to infer one or more categories. It may then request that the advertiser confirm or deny some basic feedback information (e.g., categories, Webpage information, etc.). For example, an advertiser may be provided with candidate categories and may be asked to confirm (e.g., using checkboxes) which of the categories are relevant to their ad. Keywords may be determined using at least the categories. The determined keywords may be provided to the advertiser as suggested keywords, or may automatically populate ad serving constraint information as targeting keywords.

Abstract: An ordering of advertisements requested by an ad consumer is based on scores generated for the ads. In each case, a score may be a function of at least one performance parameter associated with the ad. The ad consumer may generate presentation information (e.g., a Web page) including advertisements and non-advertisement content (e.g., search results) by (i) accepting the non-advertisement content and advertisements, each of the advertisements including a score, and (ii) combining the non-advertisement content and the advertisements such that relative placements of the advertisements are based on their associated scores.

Abstract: Targeting information (also referred to as ad “serving constraints”) or candidate targeting information for an advertisement is identified. Targeting information may be identified by extracting topics or concepts from, and/or generating topics or concepts based on, ad information, such as information from a Web page to which an ad is linked (or some other Web page of interest to the ad or advertiser). The topics or concepts may be relevant queries associated with the Web page of interest, clusters, etc.

Abstract: Targeting information (also referred to as ad “serving constraints”) or candidate targeting information for an advertisement is identified. Targeting information may be identified by extracting topics or concepts from, and/or generating topics or concepts based on, ad information, such as information from a Web page to which an ad is linked (or some other Web page of interest to the ad or advertiser). The topics or concepts may be relevant queries associated with the Web page of interest, clusters, etc.

Abstract: A method optimizes a predictive computation model efficiently. The method includes (i) selecting model parameters that are expected to take real values within a one-sided predetermined range; and (ii) iteratively: (a) receiving a set of input values; (b) executing the computation model based on the input values; (c) updating the values of the model parameters to minimize a loss function; and (d) examining each of the model parameters, such that, when the examined model parameter attains or moves past a value that is idempotent to the computation model, removing the model parameter from the computation model. In one embodiment, the predetermined range is either the range between a predetermined positive real value and positive infinity or the range between a predetermined negative real value and negative infinity. The predetermined positive real value or the predetermined negative real value may be an idempotent value to the model computation.

Abstract: A method provides program structures for constructing a program that is learned over training data. In one example, two specific program structures are provided in which the first program structure transforms each vector in an input tuple of vectors to provide an output tuple of vectors, and the second program structure operates on an input tuple of vectors to provide an output tuple of vectors by applying one or more transformations that each involves two or more vectors in the input tuple. The transformations of the first and second program structures may be linear transformations. The program may alternatively execute the first program structure and the second program structure in any suitable order a number of times, beginning with operating one of the program structures on an initial tuple of vectors. The vectors may each consist of an ordered set of real numbers.

Abstract: A system includes a number of different long short term memory (LSTM) elements, in which the input value of each LSTM element is gated by a different number of input control signals. In addition, each LSTM element may also include a state feed-back path in which the current state is weighted by a function of a product of one or more memory control values.