Abstract: Methods, systems, and computer-readable storage media for a machine learning (ML) system for matching a query entity to one or more target entities, the ML system that reducing a number of query-target entity pairs from consideration as potential matches during inference.

Abstract: Methods, systems, and computer-readable storage media for a ML system that reduces a number of target items from consideration as potential matches to a query item using token embeddings and a search tree.

Abstract: Methods, systems, and computer-readable storage media for receiving a set of inference results generated by a ML model, the inference results including a set of query entities and a set of target entities, each query entity having one or more target entities matched thereto by the ML model, processing the set of inference results to generate a set of matched sub-sets of target entities by executing a search over target entities in the set of target entities based on constraints, for each problem in a set of problems, providing the problem as a tuple including an index value representative of a target entity in the set of target entities and a value associated with the query entity, the value including a constraint relative to the query entity, and executing at least one task in response to one or more matched sub-sets in the set of matched sub-sets.

Abstract: Methods, systems, and computer-readable storage media for receiving a first set of predictions generated by a ML model during execution of a training pipeline to train the ML model, each prediction in the first set of predictions being associated with a confidence, determining a set of confidence bins based on confidences of the first set of predictions, for each confidence bin in the set of confidence bins, providing an accuracy, processing the set of confidence bins and accuracies through a regression model to provide one or more regressions, each regression representing a confidence-to-accuracy relationship, defining a set of confidence thresholds based on at least one regression of the one or more regressions, and during an inference phase, applying the set of confidence thresholds to selectively filter predictions from a second set of predictions generated by the ML model.

Abstract: Methods, systems, and computer-readable storage media for receiving a set of inference results generated by a ML model, the inference results including a set of query entities and a set of target entities, each query entity having one or more target entities matched thereto by the ML model, processing the set of inference results to generate a set of matched sub-sets of target entities by executing a search over target entities in the set of target entities based on constraints, for each problem in a set of problems, providing the problem as a tuple including an index value representative of a target entity in the set of target entities and a value associated with the query entity, the value including a constraint relative to the query entity, and executing at least one task in response to one or more matched sub-sets in the set of matched sub-sets.

Abstract: Methods, systems, and computer-readable storage media for receiving IRF data sets, the IRF data sets including a set of records including inference results determined by the ML model during production use of the ML model and at least one correction to an inference result, executing incremental training of the ML model to provide an updated ML model by selectively filtering one or more records of the set of records to adjust a negative sample to positive sample proportion of a sub-set of records based on a negative sample to positive sample proportion of initial training of the ML model, for each record in the sub-set of records, determining a weight, and during incremental training, applying the weight of a respective record being in a loss function in determining an accuracy of the ML model based on the respective record, and deploying the updated ML model for production use.

Abstract: Embodiments provide a system for controlling HVAC/ACMV system of a building, including an occupancy pattern extractor configured to generate at least one facility-based occupancy pattern for each facility type based on historical occupancy data and spatial information of the building; a zone occupancy predictor configured to predict zone occupancy variation of each zone after a predetermined time period, based on the facility-based occupancy patterns and real-time occupancy data; a similar zone matcher configured to match each zone with one or more pre-stored zones and determine air handler configurations based on the matched pre-stored zones; a configuration generator configured to determine configuration combinations by combining the air handler configurations for a plurality of zones of the building, each configuration combination including one of the air handler configurations for each zone; and a configuration optimizer configured to determine an optimal configuration combination based on one or more key

Abstract: Embodiments provide a system for controlling HVAC/ACMV system of a building, including an occupancy pattern extractor configured to generate at least one facility-based occupancy pattern for each facility type based on historical occupancy data and spatial information of the building; a zone occupancy predictor configured to predict zone occupancy variation of each zone after a predetermined time period, based on the facility-based occupancy patterns and real-time occupancy data; a similar zone matcher configured to match each zone with one or more pre-stored zones and determine air handler configurations based on the matched pre-stored zones; a configuration generator configured to determine configuration combinations by combining the air handler configurations for a plurality of zones of the building, each configuration combination including one of the air handler configurations for each zone; and a configuration optimizer configured to determine an optimal configuration combination based on one or more key

Abstract: Methods, systems, and computer-readable storage media for receiving, by a machine learning (ML) platform, a set of invoices including two or more invoices, processing, by the ML platform, each invoice through a neural network to provide respective invoice embeddings, each invoice embedding including a multi-dimensional vector, comparing, by the ML platform, invoice embeddings to define two or more super-invoices, each super-invoice including a sub-set of the set of invoices, and matching a bank statement to a super-invoice of the two or more super-invoices.

Abstract: According to various embodiments, there is provided a passenger load prediction system including: a component configured to detect a wireless device carried by a passenger on a train or a train station platform, the train including a plurality of train cars; a passenger to train car mapper configured to determine a location of the passenger, based on a location of the wireless device; a destination predictor configured to predict a destination of the passenger, based at least in part on an identifier code of the wireless device; and a train car load level estimator configured to predict a respective passenger load of each train car of the plurality of train cars, based on the predicted destination and further based on the determined location of the passenger.

Abstract: According to various embodiments, there is provided a passenger load prediction system including: a component configured to detect a wireless device carried by a passenger on a train or a train station platform, the train including a plurality of train cars; a passenger to train car mapper configured to determine a location of the passenger, based on a location of the wireless device; a destination predictor configured to predict a destination of the passenger, based at least in part on an identifier code of the wireless device; and a train car load level estimator configured to predict a respective passenger load of each train car of the plurality of train cars, based on the predicted destination and further based on the determined location of the passenger.

Abstract: Real-time image processing and annotation of video streams is provided by a system of plural processors and memory storing executable instructions to cause the processors to execute real-time processing. Different regions are set for frames of the video stream which define objects therein based on the context or content thereof. Skipping intervals are set for each region. Frames are individually selected from the video stream according to each skipping interval of each region. Specific regions are separately processed by different processors in the selected frames which are separated at intervals within the video stream by the frame skipping values. The processing of the regions identifies objects therein and stores descriptions of the objects in an index to facilitate searching of the video content. The activity of the objects in the video stream further cause the frame skipping levels to change thereby causing selected individual frames to be dynamically processed.

Abstract: Some examples include a plurality of nodes configured to execute map-reduce jobs by enabling tasks to share processing slots with other tasks. As one example, a job tracker may compare a task profile for a received task with one or more task profiles for one or more respective tasks already assigned for execution on the processing slots of one or more worker nodes. Based at least in part on the comparing, the job tracker may select a particular one of already assigned tasks to be executed concurrently with the received task on a slot. In addition, the job tracker may determine one or more expected future tasks based at least in part on one or more ongoing workflows of map-reduce jobs. The selection of the already assigned task to be executed concurrently with the received task may also be based in part on the expected future tasks.