Abstract: Systems and methods are described for generating record clusters. The methods comprise receiving a plurality of records from data sources and providing at least a subset of the records to a scoring model that determines scores for various pairings of the records, a score for a given pair of the records representing a probability that the given pair of records contain data elements about the same entity. The method further comprises generating a graph data structure that includes a plurality of nodes, individual nodes representing a different record from the records. The method also comprises assigning a different unique identifier to individual clusters of the final clusters and responding to a request for data regarding a given entity by providing aggregated data elements from those records of the records associated with a cluster of the final clusters having an identifier that represents the given entity.

Abstract: Computer vision systems and methods for optimizing correlation clustering for image segmentation are provided. The system receives input data and generates a correlation clustering formulation for Benders Decomposition for optimized correlation clustering of the input data. The system optimizes the Benders Decomposition for the generated correlation clustering formulation and performs image segmentation using the optimized Benders Decomposition.

Abstract: Disclosed are devices, systems and methods for routing and scheduling based on family column generation (FCG). The described embodiments provide a dual stabilization method that accelerates column generation (CG), thereby decreasing the number of iterations of CG needed to solve the problem. An example method includes receiving information associated with each of the plurality of facilities, receiving a plurality of demands associated with each of the plurality of customers, generating, based on the information and the plurality of demands, a facility location problem, splitting the facility location problem into a master problem and a subproblem, and iteratively solving, using a FCG method, the master problem and the subproblem to generate the solution comprising assignments between the plurality of customers and the subset of the plurality of facilities that minimize the total cost.

Abstract: Disclosed are devices, systems, and methods for routing and scheduling using column generation (CG)-based techniques. The described embodiments include dual optimal inequalities that incorporate detours (referred to as detour-DOI), graph generation (GG), principled graph management (PGM), and column generation with local area (LA) route relaxations. These CG-based techniques can be applied to a variety of logistical optimization problems that include, but are not limited to, the facility location problem, the capacitated vehicle routing problem (CVRP), the CVRP with time windows, multi-robot routing (both with and without time windows), the bus driver scheduling problem, the supply chain scheduling problem, the shift/workforce scheduling problem, and the grocery picking for micro-fulfillment problem.

Abstract: Systems and methods for controlling automated systems, such as robots and other devices/resources, using integer programming and column generation techniques, are provided. The system retrieves location and movement data corresponding to at least one automated system, and processes the location and movement data using an integer linear programming algorithm. The system also optimizes the integer linear programming algorithm using a column generation algorithm, and solves a pricing problem associated with generating routes for the at least one automated system. Then, the system generates a route based on outputs of the integer linear programming algorithm, the column generation algorithm, and solving of the pricing problem, and transmits the route to the at least one automated system, the at least one automated system executing the route to control a location or a movement of the at least one automated system.

Abstract: Systems and methods for automated vehicle routing using column generation optimization are provided. The system receives capacitated vehicle routing problem (CVRP) input data and generates a minimum weight set cover problem formulation for a CVRP for performing column generation optimization over the input data. The system determines smooth-dual optimal inequalities (S-DOI) and flexible-dual optimal inequalities (F-DOI) for the CVRP for performing the column generation optimization over a valid subset of the input data. Then, the system adapts the S-DOI and the F-DOI to generate smooth and flexible dual optimal inequalities (SF-DOI) for the CVRP for performing the column generation optimization over a relaxed subset of the input data. The system utilizes the SF-DOI to accelerate column generation optimization over the relaxed subset of the input data.

Abstract: Computer vision systems and methods for optimizing correlation clustering for image segmentation are provided. The system receives input data and generates a correlation clustering formulation for Benders Decomposition for optimized correlation clustering of the input data. The system optimizes the Benders Decomposition for the generated correlation clustering formulation and performs image segmentation using the optimized Benders Decomposition.

Abstract: Computer vision systems and methods for end-to end training of neural networks are provided. The system generates a fixed point algorithm for dual-decomposition of a maximum-a-posteriori inference problem and trains the convolutional neural network and a conditional random field with the fixed point algorithm and a plurality of images of a dataset to learn to perform semantic image segmentation. The system can segment an attribute of an image of the dataset by the trained neural network and the conditional random field.

Abstract: Machine learning systems and methods for performing entity resolution. The system receives a dataset of observations and utilizes a machine learning algorithm to apply a blocking technique to the dataset to identify and generate a subset of pairs of observations of the dataset that could represent a same real world entity. The system generates a probability score for each pair of observations of the subset where the probability score is defined over a given pair of observations and denotes a probability that each pair is associated with a common entity in ground truth. The system utilizes a flexible minimum weight set packing framework to determine problem specific cost terms of a single hypothesis associated with the subset of pairs of observations and to perform entity resolution by partitioning the subset of pairs of observations into hypotheses based on the cost terms.

Abstract: Computer vision systems and methods for machine learning using a set packing framework are provided. A minimum weight set packing (“MWSP”) framework is parameterized by a set of possible hypotheses, each of which is associated with a real valued cost that describes the sensibility of the belief that the members of the hypothesis correspond to a common cause. Using MWSP, the system then selects the lowest total cost set of hypotheses, such that no two selected hypotheses share a common observation. Observations that are not included in any selected hypothesis, define the set of false observations can be thought of as false observations/noise. The system can be utilized to support one or more trained computer models in performing computer vision on input data in order to generate output data.