Abstract: The technology described herein determines whether a candidate text is in a requested class by using a generative model that may not be trained on the requested class. The present technology may use of a model trained primarily in an unsupervised mode, without requiring a large number of manual user-input examples of a label class. The may produce a semantically rich positive example of label text from a candidate text and label. Likewise, the technology may produce from the candidate text and the label a semantically rich negative example of label text. The labeling service makes use of a generative model to produce a generative result, which estimates the likelihood that the label properly applies to the candidate text. In another aspect, the technology is directed toward a method for obtaining a semantically rich example that is similar to a candidate text.

Abstract: Computerized systems are provided for detecting one or more tables and performing information extraction and analysis on any given table. Information can be extracted from one or more cells or fields of a table and feature vectors representing individual cells, rows, and/or columns of the table can be derived and concatenated together. In this way, embodiments can use some or all of the “context” or values contained in various feature vectors representing some or all of a single table as signals or factors to consider when generating a decision statistic, such as a classification prediction, for a particular cell.

Abstract: The technology described herein determines whether a candidate text is in a requested class by using a generative model that may not be trained on the requested class. The present technology may use of a model trained primarily in an unsupervised mode, without requiring a large number of manual user-input examples of a label class. The may produce a semantically rich positive example of label text from a candidate text and label. Likewise, the technology may produce from the candidate text and the label a semantically rich negative example of label text. The labeling service makes use of a generative model to produce a generative result, which estimates the likelihood that the label properly applies to the candidate text. In another aspect, the technology is directed toward a method for obtaining a semantically rich example that is similar to a candidate text.

Abstract: Computerized systems are provided for detecting one or more tables and performing information extraction and analysis on any given table. Information can be extracted from one or more cells or fields of a table and feature vectors representing individual cells, rows, and/or columns of the table can be derived and concatenated together. In this way, embodiments can use some or all of the “context” or values contained in various feature vectors representing some or all of a single table as signals or factors to consider when generating a decision statistic, such as a classification prediction, for a particular cell.

Abstract: Computerized systems are provided for detecting one or more tables and performing information extraction and analysis on any given table. Information can be extracted from one or more cells or fields of a table and feature vectors representing individual cells, rows, and/or columns of the table can be derived and concatenated together. In this way, embodiments can use some or all of the “context” or values contained in various feature vectors representing some or all of a single table as signals or factors to consider when generating a decision statistic, such as a classification prediction, for a particular cell.

Abstract: The technology described herein determines whether a candidate text is in a requested class by using a generative model that may not be trained on the requested class. The present technology may use of a model trained primarily in an unsupervised mode, without requiring a large number of manual user-input examples of a label class. The may produce a semantically rich positive example of label text from a candidate text and label. Likewise, the technology may produce from the candidate text and the label a semantically rich negative example of label text. The labeling service makes use of a generative model to produce a generative result, which estimates the likelihood that the label properly applies to the candidate text. In another aspect, the technology is directed toward a method for obtaining a semantically rich example that is similar to a candidate text.

Abstract: Computerized systems are provided for detecting one or more tables and performing information extraction and analysis on any given table. Information can be extracted from one or more cells or fields of a table and feature vectors representing individual cells, rows, and/or columns of the table can be derived and concatenated together. In this way, embodiments can use some or all of the “context” or values contained in various feature vectors representing some or all of a single table as signals or factors to consider when generating a decision statistic, such as a classification prediction, for a particular cell.

Abstract: In one embodiment, a current source circuit having a reference resistor produces first, second and third bias currents that vary with manufacturing variances of the current compensation resistor. An input amplification stage includes a transconductance stage biased by the first bias current, a first transimpedance amplifier (TIA) biased by the second bias current, and a first feedback resistor coupled between the first TIA's input and output. The input of the first TIA is coupled to an output of the transconductance stage. An output amplification stage is biased by the third bias current and has an input coupled to an output of the first TIA. A second feedback resistor is coupled between the output of the output amplification stage and the input of the transconductance stage. The reference resistor and first and second feedback resistors are formed using a common manufacturing process.

Abstract: A first pattern (64) of candy beads (14) is picked up by use of a vacuum carrier (54) and is positioned on a region of the upper surface of a sheet candy member (12). The vacuum is turned off and the vacuum carrier (54) is moved away from the sheet candy member (12) leaving the pattern (64) of candy beads (14) on the sheet candy member (12). This procedure is repeated at least once, and preferably two more times, providing an additional pattern or pattern of different colored candy beads (16, 18) on the upper surface of the base candy member (12). The color of the base candy member (12) and the color of the different candy beads (14, 16, 18) together form a picture or a design on the sheet candy member (12).