Pawan Kumar Mudigonda

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This paper proposes an adaptive neural-compilation framework to address the problem of efficient program learning. Traditional code optimisation strategies used in compilers are based on applying pre-specified set of transformations that make the code faster to execute without changing its semantics. In contrast, our work involves adapting programs to make(More)
This paper presents several results on images of various configurations of conics. We extract information about the plane from single and multiple views of known and unknown conics, based on planar homography and conic correspondences. We show that a single conic section cannot provide sufficient information. Metric rectification of the plane can be(More)
This thesis investigates the role of optimization in two areas of Computer Science: Computer Vision and Machine Learning. Specifically, we consider two well-known problems in Computer Vision, namely motion segmentation and object category specific image segmentation, and a fundamental problem in Machine Learning, known as maximum a posteriori (map)(More)
The problem of obtaining the maximum a posteriori estimate of a general discrete random field (i.e. a random field defined using a finite and discrete set of labels) is known to be NP-hard. However, due to its central importance in many applications, several approximate algorithms have been proposed in the literature. In this paper, we present an analysis(More)
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