Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein are directed to a Mapping Engine that selects a candidate job title(s) from a portion of a job title taxonomy that corresponds with a job title(s) in profile data of a target member account of a social network service. For each respective candidate job title in the plurality of candidate job titles, the Mapping Engine assembles, according to an encoded rule(s) of a machine learning model for the respective candidate job title, feature vector data based in part on profile data of the target member account. The Mapping Engine calculates a probable job title score according to the machine learning model for the respective candidate job title. The Mapping Engine identifies a select probable job title score from a plurality of probable job title scores.

Abstract: In an example embodiment, a solution that automatically predicts an industry for a candidate company is provided. An existing industry classifier is trained using a first machine learning algorithm, the first machine learning algorithm taking as input first training data and existing industries listed in an industry taxonomy. A new industry classifier is trained using a second machine learning algorithm, the second machine learning algorithm taking as input second training data and new industries listed in an industry taxonomy. Then the candidate company is fed into the existing industry classifier, producing one or more predicted existing industries corresponding to the candidate company. The candidate company is also fed into the new industry classifier, producing one or more predicted new industries corresponding to the candidate company. One or more final predicted industries are selected from among the one or more predicted existing industries and the one or more predicted new industries.

Abstract: In an example embodiment, for each of one or more input documents: a first value is determined for the first metric for a first transformation of the input document by passing the first transformation to s first Deep Convolutional Neural Network (DCNN), a second transformation of the input document is determined by passing the input document to a second DCNN, the second transformation of the input document is passed to the first DCNN, obtaining a second value for the first metric for the second transformation of the input document, the first and second transformations being of the first transformation type, and a difference between the first value and the second value for the input document is determined. Then it is determined whether to change the system over from the first DCNN to the second DCNN based on the difference between the first value and the second value.

Abstract: In an example embodiment, a first DCNN is trained to output a value for a first metric by inputting a plurality of sample documents to the first DCNN, with each of the sample documents having been labeled with a value for the first metric. Then a plurality of possible transformations of a first input document are fed to the first DCNN, obtaining a value for the first metric for each of the plurality of possible transformations. A first transformation is selected from the plurality of possible transformations based on the values for the first metric for each of the plurality of possible transformations. Then a second DCNN is trained to output a transformation for a document by inputting the selected first transformation to the second DCNN. The second input document is fed to the second DCNN, obtaining a second transformation of the second input document.

Abstract: In an example embodiment, a first plurality of images stored on a computing device is identified, each image having an indication that it depicts a first member of a social networking service. The first plurality of images is used as training data to a first machine learning algorithm to train a first machine learning algorithm model corresponding to the first member, the first machine learning algorithm model corresponding to the first member designed to calculate a member likelihood score for a candidate image. Then a second plurality of images stored on the computing device is obtained. Each image of the second plurality of images is fed to the first machine learning algorithm model corresponding to the first member, obtaining a member likelihood score for each of the second plurality of images. Then, based on the member likelihood scores for the second plurality of images, one or more member images are selected.

Abstract: In an example embodiment, a deep convolutional neural network (DCNN) is created to assign a professionalism score to an input image. The professionalism score indicates a perceived professionalism of a subject of the input image. The DCNN is designed to automatically learn features of images relevant to the professionalism through a training process.

Abstract: In an example embodiment, an a loss layer of a deep convolutional neural network is modified to include a dynamically changing function that adjusts based on statistical analysis of the samples, and specifically an analysis of which sample images showed the most deviation between their assigned professionalism score and an expected professionalism score. This allows outliers in training data to be automatically handled.

Abstract: In an example embodiment, an image transformation is automatically performed on a digital image to improve perceived professionalism of a subject of the image. A machine learning algorithm is utilized to generate a professionalism score for the digital image, the utilizing a machine learning algorithm comprising: a training mode where a plurality of sample images with labeled professionalism scores are used to train a classification function in a model that produces as professionalism score as output; an analysis mode where the model is used to generate a professionalism score for the digital image. Then the professionalism score is used as an input to a continuous variable optimization algorithm to determine an optimum version of the digital image from a plurality of possible versions of the digital image on which one or more image transformations have been performed, using the classification function.

Abstract: In an example embodiment, an optimal cropping of a digital image is determined. A machine learning algorithm is used to generate a professionalism score for the digital image, the utilizing a machine learning algorithm comprising a training mode where a plurality of sample images with labeled professionalism scores are used to train a classification function in a model that produces as professionalism score as output; and an analysis mode where the model is used to generate a professionalism score for the digital image. Then, the professionalism score is used as an input to a discrete variable optimization algorithm to determine an optimum cropped version of the digital image from a plurality of possible cropped versions of the digital image using the classification function.

Abstract: Automated image analysis used in vascular state modeling. Coronary vasculature in particular is modeled in some embodiments. Methods of “virtual revascularization” of a presently stenotic vasculature are described; useful, for example, as a reference in disease state determinations. Structure and uses of a model which relates records comprising acquired images or other structured data to a vascular tree representation are described.

Abstract: A profile update evaluator is configured to detect a change to profile data in a member profile and determine whether the change can be viewed as indicative of a positive professional transition for a member represented by the profile. If the change can be viewed as indicative of a positive professional transition, the profile update evaluator publishes the update to the member's network. Absent an indication that the change can be viewed as indicative of a positive professional transition, the profile update evaluator does not publish the update to the member's network and does not invite the member's connections to congratulate her on the job change.

Abstract: In order to determine seniority associated with a title string associated with a member profile in an on-line social network system, a standardization system may be configured to operate as follows. A standardization system may determine a canonical title that corresponds to the title string, determine any seniority modifiers that may be present in the title string, and calculate a seniority value for the title sting as the sum of the seniority value assigned to the determined canonical title and the respective seniority values of the determined seniority modifiers. A seniority modifier is a phrase comprising one or more words that have been identified as being indicative of seniority if included in a title string.

Abstract: A title standardization system may be configured to automatically identify modifier terms in title strings and store these terms in a dictionary for future use. Modifier terms are those phrases in a title string that have been identified as indicative of a certain aspect related to the job of the associated member. In order to identify modifier terms, a title standardization system examines transitions between jobs that the members of the on-line social network system have reported via their respective profiles. If a transition pair comprising a first title string and a second title string was determined to be conforming to a stable pattern, a phrase that is included in the first title string and is lacking from the second title string is identified as a modifier phrase and stored in a dictionary for future use.

Abstract: A seniority standardization system may be configured to derive seniority values in the context of an on-line social network system. In order to determine a seniority rank of a given professional title, a seniority standardization system may leverage transition data, which is information that may be gleaned from a member profile with respect to the member's transition from one professional position to another. A seniority standardization system may also use time-based seniority signal. A time-based seniority value, which may be assigned to a particular professional title, is the amount of time that it typically takes to achieve a professional position represented by that particular professional title.

Abstract: A title standardization system is may be configured to detect an edit operation associated with the job title field of a member profile stored by an on-line social network system and, in response, perform operations to derive a canonical title that represents a raw title string found in the job title field. The derived canonical title may be then associated with the member profile, in which the originally-obtained subject title string was found. This association may be stored in a database for future use, e.g., for targeting job recommendations, recruiting, making professional contacts, as well as for other purposes.

Abstract: Automated image analysis used in vascular disease characterization. Coronary vasculature in particular is automatically characterized in some embodiments for lesion complexity and related anatomical and/or functional parameters related to disease state. In some embodiments, a “virtual revascularization” model is used as a reference in disease state determinations. In some embodiments, disease parameters are determined for application by a disease characterization tool such as a SYNTAX Score calculator.