Abstract: A method for rejecting biased data using a machine learning model includes receiving a cluster training data set including a known unbiased population of data and training a clustering model to segment the received cluster training data set into clusters based on data characteristics of the known unbiased population of data. Each cluster of the cluster training data set includes a cluster weight. The method also includes receiving a training data set for a machine learning model and generating training data set weights corresponding to the training data set for the machine learning model based on the clustering model. The method also includes adjusting each training data set weight of the training data set weights to match a respective cluster weight and providing the adjusted training data set to the machine learning model as an unbiased training data set.

Abstract: A cementing system includes an inner string configured to be received into a casing string in a wellbore. The cementing system also includes a stinger coupled to the inner string and configured to be received into a float shoe connected to the casing string. The cementing system also includes a first retractable joint coupled to the inner string. The retractable joint is configured to reduce in axial length so as to permit at least part of stinger to be withdrawn from engagement with the float shoe while a weight of the inner string is supported by a hanger running tool and casing hanger connected to the casing. The inner string, the stinger, and the retractable joint form at least a part of a flowpath that extends through an end of the casing string at the float shoe.

Abstract: Systems and methods described herein may include a memory and a computing a system in communication with said memory. The computing system may be configured to receive data from network management systems. In one embodiment, the network management system includes a network gateway. Users at venues may access external network resources using the network management system. Further, the network management systems may extract device identifiers from network packets sent from user devices to request access to external network resources. In some embodiments, the network management system may provide transmission control protocol handshake completion data to user devices. In some embodiments, the computing system also receives one or more attributes associated with the venue, user data associated with the user device, and connection data associated with communication between the user device and said external network resource.

Abstract: Implementations relate to providing, in response to a query, machine learning model output that is based on output from a trained machine learning model. The machine learning model output can include a predicted answer to the query, that is predicted based on the trained machine learning model. The machine learning model output can additionally or alternatively include an interactive interface for the trained machine learning model. Some implementations relate to generating a trained machine learning model “on the fly” based on a search query. Some implementations additionally or alternatively relate to storing, in a search index, an association of a machine learning model with a plurality of content items from resource(s) on which the machine learning model was trained.

Abstract: Systems and methods described herein may include a memory and a computing a system in communication with said memory. The computing system may be configured to receive data from network management systems. In one embodiment, the network management system includes a network gateway. Users at venues may access external network resources using the network management system. Further, the network management systems may extract device identifiers from network packets sent from user devices to request access to external network resources. In some embodiments, the network management system may provide transmission control protocol handshake completion data to user devices. In some embodiments, the computing system also receives one or more attributes associated with the venue, user data associated with the user device, and connection data associated with communication between the user device and said external network resource.

Abstract: Implementations relate to providing, in response to a query, machine learning model output that is based on output from a trained machine learning model. The machine learning model output can include a predicted answer to the query, that is predicted based on the trained machine learning model. The machine learning model output can additionally or alternatively include an interactive interface for the trained machine learning model. Some implementations relate to generating a trained machine learning model “on the fly” based on a search query. Some implementations additionally or alternatively relate to storing, in a search index, an association of a machine learning model with a plurality of content items from resource(s) on which the machine learning model was trained.

Abstract: Systems and methods described herein may include a memory and a computing a system in communication with said memory. The computing system may be configured to receive data from network management systems. In one embodiment, the network management system includes a network gateway. Users at venues may access external network resources using the network management system. Further, the network management systems may extract device identifiers from network packets sent from user devices to request access to external network resources. In some embodiments, the network management system may provide transmission control protocol handshake completion data to user devices. In some embodiments, the computing system also receives one or more attributes associated with the venue, user data associated with the user device, and connection data associated with communication between the user device and said external network resource.

Abstract: A method for rejecting biased data includes receiving a bias training data set based on a probability distribution of bias-sensitive variables of a target population and segmenting the bias training data set into clusters based on at least one respective bias-sensitive variable of the target population, each cluster including a bias cluster weight. The method also includes receiving a training data set for a machine learning model and segmenting the training data set into training clusters. Each training cluster is associated with at least one corresponding bias-sensitive variable of the target population and includes a corresponding training data set weight. The method also includes adjusting each training data set weight to match a respective bias cluster weight to form an adjusted training data set and providing the adjusted training data set to the machine learning model as an unbiased training data set.

Abstract: A method for rejecting biased data using a machine learning model includes receiving a cluster training data set including a known unbiased population of data and training a clustering model to segment the received cluster training data set into clusters based on data characteristics of the known unbiased population of data. Each cluster of the cluster training data set includes a cluster weight. The method also includes receiving a training data set for a machine learning model and generating training data set weights corresponding to the training data set for the machine learning model based on the clustering model. The method also includes adjusting each training data set weight of the training data set weights to match a respective cluster weight and providing the adjusted training data set to the machine learning model as an unbiased training data set.

Abstract: A method for rejecting biased data using a machine learning model includes receiving a cluster training data set including a known unbiased population of data and training a clustering model to segment the received cluster training data set into clusters based on data characteristics of the known unbiased population of data. Each cluster of the cluster training data set includes a cluster weight. The method also includes receiving a training data set for a machine learning model and generating training data set weights corresponding to the training data set for the machine learning model based on the clustering model. The method also includes adjusting each training data set weight of the training data set weights to match a respective cluster weight and providing the adjusted training data set to the machine learning model as an unbiased training data set.

Abstract: A method for rejecting biased data includes receiving a bias training data set based on a probability distribution of bias-sensitive variables of a target population and segmenting the bias training data set into clusters based on at least one respective bias-sensitive variable of the target population, each cluster including a bias cluster weight. The method also includes receiving a training data set for a machine learning model and segmenting the training data set into training clusters. Each training cluster is associated with at least one corresponding bias-sensitive variable of the target population and includes a corresponding training data set weight. The method also includes adjusting each training data set weight to match a respective bias cluster weight to form an adjusted training data set and providing the adjusted training data set to the machine learning model as an unbiased training data set.

Abstract: A method for rejecting biased data using a machine learning model includes receiving a cluster training data set including a known unbiased population of data and training a clustering model to segment the received cluster training data set into clusters based on data characteristics of the known unbiased population of data. Each cluster of the cluster training data set includes a cluster weight. The method also includes receiving a training data set for a machine learning model and generating training data set weights corresponding to the training data set for the machine learning model based on the clustering model. The method also includes adjusting each training data set weight of the training data set weights to match a respective cluster weight and providing the adjusted training data set to the machine learning model as an unbiased training data set.

Abstract: Systems and methods described herein may include a memory and a computing a system in communication with said memory. The computing system may be configured to receive data from network management systems. In one embodiment, the network management system includes a network gateway. Users at venues may access external network resources using the network management system. Further, the network management systems may extract device identifiers from network packets sent from user devices to request access to external network resources. In some embodiments, the network management system may provide transmission control protocol handshake completion data to user devices. In some embodiments, the computing system also receives one or more attributes associated with the venue, user data associated with the user device, and connection data associated with communication between the user device and said external network resource.

Abstract: Implementations relate to providing, in response to a query, machine learning model output that is based on output from a trained machine learning model. The machine learning model output can include a predicted answer to the query, that is predicted based on the trained machine learning model. The machine learning model output can additionally or alternatively include an interactive interface for the trained machine learning model. Some implementations relate to generating a trained machine learning model “on the fly” based on a search query. Some implementations additionally or alternatively relate to storing, in a search index, an association of a machine learning model with a plurality of content items from resource(s) on which the machine learning model was trained.

Abstract: Systems and methods described herein may include a memory and a computing a system in communication with said memory. The computing system may be configured to receive data from network management systems. In one embodiment, the network management system includes a network gateway. Users at venues may access external network resources using the network management system. Further, the network management systems may extract device identifiers from network packets sent from user devices to request access to external network resources. In some embodiments, the network management system may provide transmission control protocol handshake completion data to user devices. In some embodiments, the computing system also receives one or more attributes associated with the venue, user data associated with the user device, and connection data associated with communication between the user device and said external network resource.

Abstract: Systems and methods described herein may include a memory and a computing a system in communication with said memory. The computing system may be configured to receive data from network management systems. In one embodiment, the network management system includes a network gateway. Users at venues may access external network resources using the network management system. Further, the network management systems may extract device identifiers from network packets sent from user devices to request access to external network resources. In some embodiments, the network management system may provide transmission control protocol handshake completion data to user devices. In some embodiments, the computing system also receives one or more attributes associated with the venue, user data associated with the user device, and connection data associated with communication between the user device and said external network resource.