[PDF] Top 20 Determinantal Point Processes for Coresets
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Determinantal Point Processes for Coresets
... We start by showing in Figure 4 the results of m-DPP versus the number of dimensions and the choice of parameter τ for the Gaussian kernel. All shown results are with q = 0 (no outlier) and with a number of random ... See full document
70
Multi Document Summarization with Determinantal Point Processes and Contextualized Representations
... Determinantal point processes, shortened as DPP, is one of a number of optimization techniques that perform remarkably well in summarization com- petitions (Hong et ... See full document
6
Probabilistic Typology: Deep Generative Models of Vowel Inventories
... NLP. Determinantal point processes have found a home in the literature in tasks that require ...Poisson point processes have also been ap- plied to NLP problems: Yee et ...geneous ... See full document
11
Active Mini-Batch Sampling Using Repulsive Point Processes
... using Determinantal Point Processes (DPPs) for mini-batch diversification (Zhang et ...non-stationary point processes. We then show that other point processes may be ... See full document
8
In Conclusion Not Repetition: Comprehensive Abstractive Summarization with Diversified Attention Based on Determinantal Point Processes
... DPPs. Point subset is sampled based on quality (green) or DPPs (blue), then a gaussian mixture distribution was generated around these points to soften and reweight the ... See full document
11
Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi Document Summarization
... The most important obstacles facing multi- document summarization include excessive re- dundancy in source descriptions and the loom- ing shortage of training data. These obstacles prevent encoder-decoder models from ... See full document
12
Learning, Large Scale Inference, and Temporal Modeling of Determinantal Point Processes
... This behavior can also be explained by looking at the theory behind these two approximations. For the RFF, while the kernel approximation is guaranteed to be an unbiased estimate of the true kernel element-wise, the ... See full document
185
Determinantal Reinforcement Learning
... by determinantal point processes (DPPs) (Kulesza and Taskar 2012; Macchi 1975) and Free Energy SARSA (Sallans and Hinton 2001; 2004; Sallans ... See full document
8
On a few statistical applications of determinantal point processes
... model point patterns exhibiting inhibition, it is natural to wonder whether DPPs form a flexible class of ...Poisson point process, a peculiar case of DPP without interaction, see Section ... See full document
23
Approximate Inference for Determinantal Point Processes
... While the computational complexity of the vanilla versions of most methods, such as principal component analysis, is too high for use in settings with a large number of items and feature[r] ... See full document
164
Advances in the Theory of Determinantal Point Processes
... The first example of a determinantal point process was given in Karlin and McGregor [1959]. Assume that n independent copies of a continuous time birth- death process are started in distinct states, and ... See full document
79
Determinantal generalizations of instrumental variables
... Abstract. Linear structural equation models relate the components of a ran- dom vector using linear interdependencies and Gaussian noise. Each such model can be naturally associated with a mixed graph whose vertices ... See full document
24
The approximate determinantal assignment problem
... Moreover the authors in [Lev. & Kar. 2] have also used Schubert calculus tech- niques and the cohomology of Grassmannians, i.e., intersection theory for Grass- mannians to provide a non-factorial approach to the ... See full document
233
Training Gaussian Mixture Models at Scale via Coresets
... Several implementation choices are available. Firstly, we prove that one can use any (α, β)-bicriteria solution to k-means (α approximate with respect to the optimal k-means clustering, using βk centers). The suggested ... See full document
25
Some problems concerning cluster processes and other point processes
... Kingman (1964) has shown that a doubly stochastic Poisson process may be represented as a random time transformation of a stationary Poisson process of unit parameter. Hence it is unnecessary to prove functional limit ... See full document
165
The approximate Determinantal Assignment Problem
... approximate determinantal assignment problem has been defined and solved as a distance problem between the Grassmann variety and a linear variety defined by the properties of a desirable ...a point of the ... See full document
24
Stochastic analysis of multivariate point processes
... As we have noted in the last part of the above proof, the fidi distributions of an inf. div. m-variate point process have a compound Poisson form. Lemma 5.1 gives this result directly. It was through the ... See full document
136
Some problems concerning point processes
... interval distribution f(•) when the {q^} were Poisson, compound Poisson or binomial probabilities,while a limiting gamma process was also possible if the {q^} were negative binomial. These are somewhat surprising results ... See full document
220
Survival models for censored point processes
... Hougaard 1987 gives a good overview of the analysis of multivariate survival data, and also discusses some aspects of recurrent event data in the form of counts, and Poisson mixture mode[r] ... See full document
239
Insertion and deletion tolerance of point processes
... x ∈ [R] ∩ [0, 1) d : kx − M(x)k ≤ r . (1.3) As in [9], let X be a random variable with probability measure P ∗ and expectation operator E ∗ such that P ∗ (X ≤ r) = F(r) for all r ≥ 0 . One may interpret X as the distance ... See full document
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