Blending Learning and Inference in Structured Prediction

In this paper we derive an efficient algorithm to learn the parameters of structured predictors in general graphical models. This algorithm blends the learning and inference tasks, which results in a significant speedup over traditional approaches, such as conditional random fields and structured support vector machines. For this purpose we utilize the structures of the predictors to describe a low dimensional structured prediction task which encourages local consistencies within the different structures while learning the parameters of the model. Convexity of the learning task provides the means to enforce the consistencies between the different parts. The inference-learning blending algorithm that we propose is guaranteed to converge to the optimum of the low dimensional primal and dual programs. Unlike many of the existing approaches, the inference-learning blending allows us to learn efficiently high-order graphical models, over regions of any size, and very large number of parameters. We demonstrate the effectiveness of our approach, while presenting state-of-the-art results in stereo estimation, semantic segmentation, shape reconstruction, and indoor scene understanding

The matching relaxation for a class of generalized set partitioning problems

This paper introduces a discrete relaxation for the class of combinatorial optimization problems which can be described by a set partitioning formulation under packing constraints. We present two combinatorial relaxations based on computing maximum weighted matchings in suitable graphs. Besides providing dual bounds, the relaxations are also used on a variable reduction technique and a matheuristic. We show how that general method can be tailored to sample applications, and also perform a successful computational evaluation with benchmark instances of a problem in maritime logistics.Comment: 33 pages. A preliminary (4-page) version of this paper was presented at CTW 2016 (Cologne-Twente Workshop on Graphs and Combinatorial Optimization), with proceedings on Electronic Notes in Discrete Mathematic

Run Time Approximation of Non-blocking Service Rates for Streaming Systems

Stream processing is a compute paradigm that promises safe and efficient parallelism. Modern big-data problems are often well suited for stream processing's throughput-oriented nature. Realization of efficient stream processing requires monitoring and optimization of multiple communications links. Most techniques to optimize these links use queueing network models or network flow models, which require some idea of the actual execution rate of each independent compute kernel within the system. What we want to know is how fast can each kernel process data independent of other communicating kernels. This is known as the "service rate" of the kernel within the queueing literature. Current approaches to divining service rates are static. Modern workloads, however, are often dynamic. Shared cloud systems also present applications with highly dynamic execution environments (multiple users, hardware migration, etc.). It is therefore desirable to continuously re-tune an application during run time (online) in response to changing conditions. Our approach enables online service rate monitoring under most conditions, obviating the need for reliance on steady state predictions for what are probably non-steady state phenomena. First, some of the difficulties associated with online service rate determination are examined. Second, the algorithm to approximate the online non-blocking service rate is described. Lastly, the algorithm is implemented within the open source RaftLib framework for validation using a simple microbenchmark as well as two full streaming applications.Comment: technical repor

SimLocator: robust locator of similar objects in images

International audienceSimilar objects commonly appear in natural images, and locating and cutting out these objects can be tedious when using classical interactive image segmentation methods. In this paper, we propose SimLocator, a robust method oriented to locate and cut out similar objects with minimum user interaction. After extracting an arbitrary object template from the input image, candidate locations of similar objects are roughly detected by distinguishing the shape and color features of each image. A novel optimization method is then introduced to select accurate locations from the two sets of candidates. Additionally, a mattingbased method is used to improve the results and to ensure that all similar objects are located in the image. Finally, a method based on alpha matting is utilized to extract the precise object contours. To ensure the performance of the matting operation, this work has developed a new method for foreground extraction. Experiments show that SimLocator is more robust and more convenient to use compared to other more advanced repetition detection and interactive image segmentation methods, in terms of locating similar objects in images