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Diversifying Sparsity Using Variational Determinantal Point Processes
[article]
2014
arXiv
pre-print
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We propose a novel diverse feature selection method based on determinantal point processes (DPPs). ...
We introduce our approach in the context of Bayesian sparse regression, employing a DPP as a variational approximation to the true spike and slab posterior distribution. ...
Determinantal Point Process The determinantal point process (DPP) defines distribution over configurations of points in space. ...
arXiv:1411.6307v1
fatcat:q5dhty7zwzcc3fqfsqkp7ze5pu
Repulsive Mixture Models of Exponential Family PCA for Clustering
[article]
2020
arXiv
pre-print
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Specifically, a determinantal point process (DPP) is exploited as a diversity-encouraging prior distribution over the joint local EPCAs. ...
An efficient variational EM algorithm is derived to perform parameter learning and hidden variable inference. ...
DPP Determinantal point processes (DPP) are important statistical tools for diverse/repulsive relationship modelling. ...
arXiv:2004.03112v1
fatcat:f2dgmcwfyngh3kjuelpzhr7c6i
Exploring global diverse attention via pairwise temporal relation for video summarization
[article]
2020
arXiv
pre-print
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Most of existing systems employ encoder-decoder based recurrent neural networks, which fail to explicitly diversify the system-generated summary frames while requiring intensive computations. ...
Particularly, determinantal point process results in different frame groups revealing diversified semantics in videos. ...
[20] also developed DPP-LSTM, which further introduces Determinantal Point Process (DPP) to vsLSTM; Zhao et al. ...
arXiv:2009.10942v1
fatcat:pdv7i6ayurdrlieu5d5iu2gpvy
Hyperspectral Image Classification
[chapter]
2019
Processing and Analysis of Hyperspectral Data [Working Title]
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Hyperspectral image (HSI) classification is a phenomenal mechanism to analyze diversified land cover in remotely sensed hyperspectral images. ...
The determinantal point process (DPP) is used as a prior for probabilistic latent variable models in [68] . Probabilistic latent variable models are one of the vital elements of machine learning. ...
The determinantal point process enables a modeler to specify a notion of similarity on the space of interest, which in this case is a space of possible latent distributions, via a positive definite kernel ...
doi:10.5772/intechopen.88925
fatcat:7ixv44bobbd3vkp7hn5c6tlb2y
Diversity in Machine Learning
2019
IEEE Access
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Finally, we discuss some challenges of the diversity technology in machine learning and point out some directions in future work. ...
Many factors can affect the performance of the machine learning process, among which the diversity of the machine learning process is an important one. ...
the determinantal point process (DPP) measurement. ...
doi:10.1109/access.2019.2917620
fatcat:j7iam552ijhvvigzy5bdjwvttm
Diversified Bayesian Nonnegative Matrix Factorization
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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Specifically, the diversity prior is formulated with determinantal point processes (DPP) and is seamlessly embedded into a Bayesian NMF framework. ...
In this paper, we approach this issue using a Bayesian framework. ...
Determinantal Point Processes (DPP) Diversity is a good measure to subsets whenever the property of dissimilarity or repulsiveness is required. ...
doi:10.1609/aaai.v34i04.5991
fatcat:u2kkztzve5g5jpqrm72wy4e2ai
Testing Determinantal Point Processes
[article]
2020
arXiv
pre-print
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Determinantal point processes (DPPs) are popular probabilistic models of diversity. In this paper, we investigate DPPs from a new perspective: property testing of distributions. ...
Practical diversified recommendations on YouTube with Determinantal Point Processes. In
ACM International Conference on Information and Knowledge Management (CIKM), 2018. ...
Fixed-point algorithms for learning determinantal point processes. In
Int. Conference on Machine Learning (ICML), pages 2389-2397, 2015.
[56] Jerzy Neyman and Egon Sharpe Pearson. ...
arXiv:2008.03650v1
fatcat:acj2dbojircenixcxoz3r2sifa
"Chitty-Chitty-Chat Bot": Deep Learning for Conversational AI
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
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It is useful to diversify the candidate response list using Maximum Marginal Relevance (MMR) or Determinantal Point Processes (DPP) . ...
proposed to augment the Determinantal Point Processes (DPPs) with a Diversity Net so that the decoder selects items with good diversity and quality in balance. ...
doi:10.24963/ijcai.2018/778
dblp:conf/ijcai/Yan18
fatcat:s36y6mwhf5gapg3ila7nuh2fo4
Determinantal Point Processes for Machine Learning
2012
Foundations and Trends® in Machine Learning
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Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that arise in quantum physics and random matrix theory. ...
This subset is distributed as a determinantal point process. ...
Determinantal Point Processes Determinantal point processes (DPPs) were first identified as a class by Macchi [98] , who called them "fermion processes" because they give the distributions of fermion ...
doi:10.1561/2200000044
fatcat:qqq2fvo6tfcnbd73ee2oa43num
Diverse Counterfactual Explanations for Anomaly Detection in Time Series
[article]
2022
arXiv
pre-print
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While in the previous methods, diversity is not explicitly enforced, the DiCE algorithm [23] includes a penalization on counterfactuals' similarity based on Determinantal Point Processes. ...
We consider a general setting where time series are multivariate and the model processes all dimensions (or channels) jointly. ...
arXiv:2203.11103v1
fatcat:odumbeqcsvculm6jdakpyft6ei
Hyperspectral Image Classification – Traditional to Deep Models: A Survey for Future Prospects
[article]
2021
arXiv
pre-print
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Similarly, [192] combines multi-scale convolution-based CNN (MS-CNN) with diversified deep metrics based on determinantal point process (DPP) [193] priors for (1-D spectral, 2-D spectral-spatial, and ...
However, this training process may result in two problems: first, multiple hidden units may tend to respond similarly [228] due to co-adaptation [229] and second is linked with the sparsity and selectivity ...
arXiv:2101.06116v2
fatcat:2duwvojkybgufo4kf6sbc6hdva
A Prior Guided Adversarial Representation Learning and Hypergraph Perceptual Network for Predicting Abnormal Connections of Alzheimer's Disease
[article]
2021
arXiv
pre-print
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Alzheimer's disease is characterized by alterations of the brain's structural and functional connectivity during its progressive degenerative processes. ...
Concretely, a prior distribution from the anatomical knowledge is estimated to guide multimodal representation learning using an adversarial strategy. ...
The Determinantal Point Process(DPP) [49] based prototype learning method is adopted to select a diversified prototype subset U 0 . ...
arXiv:2110.09302v1
fatcat:al4laaigjfbk3eotv6qnkbwxom
Orthogonality-Promoting Distance Metric Learning: Convex Relaxation and Theoretical Analysis
[article]
2018
arXiv
pre-print
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projection vectors in DML to be close to being orthogonal, to achieve three effects: (1) high balancedness -- achieving comparable performance on both frequent and infrequent classes; (2) high compactness -- using ...
Determinantal Point Process [65] employed the determinantal point process (DPP) [27] as a prior to induce orthogonality in latent variable models. ...
In these works, various orthogonality-promoting regularizers have been proposed, based on determinantal point process [27, 65] and cosine similarity [62, 3, 56] . ...
arXiv:1802.06014v1
fatcat:yrz7k5ck5nbulckuyhelufm4y4
Particle EM for Variable Selection
2017
Journal of the American Statistical Association
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Motivated by non-parametric variational Bayes strategies, Particle EM achieves this goal by deploying an ensemble of interactive repulsive particles. ...
This reconstruction reflects model selection uncertainty and is supported by asymptotic considerations, which indicate that the requisite number of particles need not be large in the presence of sparsity ...
Acknowledgements The author would like to express gratitude to Ed George for providing useful feedback on earlier versions of this manuscript and to Gemma Moran for helping implement prototype versions ...
doi:10.1080/01621459.2017.1360778
fatcat:nrbc6jke6fcfvijeuygufn63oa
Diversity-Promoting and Large-Scale Machine Learning for Healthcare
2019
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In this network, a sequential LSTM is used to compose a sequence of tree LSTMs. ...
Acknowledgments
Related Works Larkey and Croft [211] studied the automatic assignment of ICD-9 codes to dictated inpatient discharge summaries, using a combination of three classifiers: k-nearest neighbors ...
[16] investigated the "diversification" of Bayesian models using the determinantal point process (DPP) [204] prior. DPP has two drawbacks. ...
doi:10.1184/r1/7553468
fatcat:ac5ifp2lnzbk3hcupr2rszxj2m
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