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Showing 1 to 8 of 8 for “"Determinantal point processes"”.
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Efficient sampling for determinantal point processes
Determinantal Point Processes (DPPs) are elegant probabilistic models of repulsion and diversity over discrete sets of items. It assigns higher probability to diverse subsets, making them more possible to be sampled. If we want to fully control the size of sampled subsets, the perfect choice would …
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Scalable Inference Algorithms for Determinantal Point Processes
Determinantal Point Processes (DPPs) are probability distributions on subsets of a collection of points that tend to generate diverse configurations of points. This feature makes them suitable as a probabilistic model of diversity. Recently this idea has been exploited extensively in subset …
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Learning and enforcing diversity with Determinantal Point Processes
… concern. Here, we approach this problem using Determinantal Point Processes (DPPs), probabilistic models that provide an intuitive and powerful way of balancing quality and diversity in sets of items. We introduce a novel, fixed-point algorithm for estimating the maximum likelihood parameters …
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Graphs, Principal Minors, and Eigenvalue Problems
… four independent topics within linear algebra: determinantal point processes, extremal problems in spectral graph theory, force-directed layouts, and eigenvalue algorithms. For determinantal point processes (DPPs), we consider the classes of symmetric and signed DPPs, respectively, and in both …
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Testing, Learning, and Optimization in High Dimensions
… is the sample complexity of testing the class of Determinantal Point Processes? and (2) Introducing a new analysis for optimization and generalization of deep neural networks beyond their linear approximation. For the first problem, we characterize the optimal sample complexity up to logarithmic …
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Characteristic polynomials of random matrices and their role in an effective theory of strong interactions
… with massive flavors, belong the class of determinantal point processes. This implies that correlation functions can be expressed as determinants of a correlation kernel. The random matrix ensembles we consider feature special biorthogonal structures leading to a sub-class of determinantal …
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Point processes in statistical mechanics : a cluster expansion approach
A point process is a mechanism, which realizes randomly locally finite point measures. One of the main results of this thesis is an existence theorem for a new class of point processes with a so called signed Levy pseudo measure L, which is an extension of the class of infinitely divisible point …
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Generative Adversarial Networks for Inverse Design Problems in Engineering: Methods to handle performance, constraints, and creativity requirements
… a singular vicinal loss combined with a Determinantal Point Processes (DPP) based loss function to enhance diversity. PcDGAN uses a new self-reinforcing score called the Lambert Log Exponential Transition Score (LLETS) for improved GAN performance on inverse design based on performance …