Structured Determinantal Point Processes

By Alex Kulesza, B. Taskar. 2010

139 citations recorded by the source corpus.

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Citations in the corpus

Determinantal Point Processes for Machine Learning (2012, methodology): “4) we can dramatically speed up computation and reduce memory use while maintaining a close approximation to the original model [83].”
Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction (2015, methodology): “Methods for inducing diversity, such as determinantal point processes [26], have been used to find diverse solutions on applications ranging from detecting objects in videos [27], topic modeling [28], and variable selection [29].”
Diverse Sequential Subset Selection for Supervised Video Summarization (2014, background): “Our focus on the determinantal point process as the building block is largely inspired by its appealing property in modeling diversity in subset selection, as well as its success in search and ranking [17], document summarization [14], news headline displaying [28], and pose estimation [29].”
Randomized Optimum Models for Structured Prediction (2012, background): “For example, Determinental Point Processes (Kulesza and Taskar, 2011) deﬁne probability models around the computation of the determinant of a matrix.”
On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbations (2013, methodology): “Other methods that efficiently produce samples include Herding [23] and determinantal processes [13].”
Individualness and Determinantal Point Processes for Pedestrian Detection (2016, methodology): “For example, a structured DPP [19] is performed by averaging the values of different feature descriptors.”
Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity (2017, methodology): “Besides its early applications in quantum physics and random matrices [35], it has also been recently applied to various machine learning tasks such as multiple-person pose estimation [27], image search [28], document summarization [29], video summarization [19], product recommendation [18], and twe”
Learning Determinantal Point Processes (2011, methodology): “Following Kulesza and Taskar (2010), we can decompose the kernel L as a Gram matrix: Lij = qiφ > i φjqj .”
Discovering Diverse and Salient Threads in Document Collections (2012, background): “While the details are beyond the scope of this paper, we note that the sampling algorithm requires O ( T n 2 D 2 ) time.”
On the Partition Function and Random Maximum A-Posteriori Perturbations (2012, methodology): “Other surrogate probability models using computational structures appears in (Papandreou & Yuille, 2010; Kulesza & Taskar, 2010).”
k-DPPs: Fixed-Size Determinantal Point Processes (2011, methodology): “Kulesza & Taskar (2010) applied DPPs to structured objects, and used them to select multiple poses of people in an image, where the poses are " diverse " in the sense that they tend not to overlap.”
A Systematic Exploration of Diversity in Machine Translation (2013, background): “Also related is work on determinantal point processes (DPPs; Kulesza and Taskar, 2010), an elegant probabilistic model over sets of items that naturally prefers diverse sets.”
Near-Optimal MAP Inference for Determinantal Point Processes (2012, methodology): “Recently, probabilistic models extending determinantal point processes (DPPs) [8, 9] were proposed for several such problems [10, 5, 11].”