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The data distribution in popular crowd counting datasets is typically heavy tailed and discontinuous. This skew affects all stages within the pipelines of deep crowd counting approaches. Specifically, the approaches exhibit unacceptably…

Computer Vision and Pattern Recognition · Computer Science 2022-04-12 Sravya Vardhani Shivapuja , Ashwin Gopinath , Ayush Gupta , Ganesh Ramakrishnan , Ravi Kiran Sarvadevabhatla

We propose a fast inference method for Bayesian nonlinear support vector machines that leverages stochastic variational inference and inducing points. Our experiments show that the proposed method is faster than competing Bayesian…

Machine Learning · Statistics 2018-03-22 Florian Wenzel , Theo Galy-Fajou , Matthaeus Deutsch , Marius Kloft

We wish to contribute to the discussion of "Comparing Consensus Monte Carlo Strategies for Distributed Bayesian Computation" by offering our views on the current best methods for Bayesian computation, both at big-data scale and with smaller…

Computation · Statistics 2017-12-15 David Draper , Alexander Terenin

Quantum computers promise to enhance machine learning for practical applications. Quantum machine learning for real-world data has to handle extensive amounts of high-dimensional data. However, conventional methods for measuring quantum…

Quantum Physics · Physics 2023-02-10 Tobias Haug , Chris N. Self , M. S. Kim

We propose a simple and efficient clustering method for high-dimensional data with a large number of clusters. Our algorithm achieves high-performance by evaluating distances of datapoints with a subset of the cluster centres. Our…

Machine Learning · Computer Science 2022-03-30 Georgios Exarchakis , Omar Oubari , Gregor Lenz

We present methods for k-means clustering on a stream with a focus on providing fast responses to clustering queries. Compared to the current state-of-the-art, our methods provide substantial improvement in the query time for cluster…

Data Structures and Algorithms · Computer Science 2018-12-10 Yu Zhang , Kanat Tangwongsan , Srikanta Tirthapura

Machine learning can provide deep insights into data, allowing machines to make high-quality predictions and having been widely used in real-world applications, such as text mining, visual classification, and recommender systems. However,…

Machine Learning · Computer Science 2020-08-11 Meng Wang , Weijie Fu , Xiangnan He , Shijie Hao , Xindong Wu

Multiple kernel learning algorithms are proposed to combine kernels in order to obtain a better similarity measure or to integrate feature representations coming from different data sources. Most of the previous research on such methods is…

Machine Learning · Computer Science 2012-07-03 Mehmet Gonen

This paper presents a significant advancement in the estimation of the Composite Link Model within a penalized likelihood framework, specifically designed to address indirect observations of grouped count data. While the model is effective…

Methodology · Statistics 2025-12-16 Carlo G. Camarda , María Durbán

Time series of counts arise in a variety of forecasting applications, for which traditional models are generally inappropriate. This paper introduces a hierarchical Bayesian formulation applicable to count time series that can easily…

Machine Learning · Statistics 2014-05-16 Nicolas Chapados

The increased availability of massive data sets provides a unique opportunity to discover subtle patterns in their distributions, but also imposes overwhelming computational challenges. To fully utilize the information contained in big…

Statistics Theory · Mathematics 2018-04-12 Stanislav Volgushev , Shih-Kang Chao , Guang Cheng

Bayesian inference is an effective approach for solving statistical learning problems especially with uncertainty and incompleteness. However, inference efficiencies are physically limited by the bottlenecks of conventional computing…

Emerging Technologies · Computer Science 2017-11-06 Xiaotao Jia , Jianlei Yang , Zhaohao Wang , Yiran Chen , Hai , Li , Weisheng Zhao

Data sets are growing in complexity thanks to the increasing facilities we have nowadays to both generate and store data. This poses many challenges to machine learning that are leading to the proposal of new methods and paradigms, in order…

Machine Learning · Computer Science 2018-12-04 Irene Córdoba , Concha Bielza , Pedro Larrañaga

Bayesian computational algorithms tend to scale poorly as data size increases. This has motivated divide-and-conquer-based approaches for scalable inference. These divide the data into subsets, perform inference for each subset in parallel,…

Methodology · Statistics 2025-10-22 Rihui Ou , Lachlan Astfalck , Deborshee Sen , David Dunson

Running Deep Neural Network (DNN) models on devices with limited computational capability is a challenge due to large compute and memory requirements. Quantized Neural Networks (QNNs) have emerged as a potential solution to this problem,…

Computer Vision and Pattern Recognition · Computer Science 2018-05-31 Yaman Umuroglu , Magnus Jahre

We address the problem of counting the number of strings in a collection where a given pattern appears, which has applications in information retrieval and data mining. Existing solutions are in a theoretical stage. We implement these…

Data Structures and Algorithms · Computer Science 2015-10-02 Travis Gagie , Aleksi Hartikainen , Juha Kärkkäinen , Gonzalo Navarro , Simon J. Puglisi , Jouni Sirén

Efficient processing of aggregated range queries on two-dimensional grids is a common requirement in information retrieval and data mining systems, for example in Geographic Information Systems and OLAP cubes. We introduce a technique to…

Data Structures and Algorithms · Computer Science 2016-04-01 Nieves R. Brisaboa , Guillermo De Bernardo , Roberto Konow , Gonzalo Navarro , Diego Seco

Clustering is a widely used technique with a long and rich history in a variety of areas. However, most existing algorithms do not scale well to large datasets, or are missing theoretical guarantees of convergence. This paper introduces a…

Machine Learning · Statistics 2024-10-16 Yijia Zhou , Kyle A. Gallivan , Adrian Barbu

Machine learning tasks over image databases often generate masks that annotate image content (e.g., saliency maps, segmentation maps, depth maps) and enable a variety of applications (e.g., determine if a model is learning spurious…

Databases · Computer Science 2024-01-09 Dong He , Jieyu Zhang , Maureen Daum , Alexander Ratner , Magdalena Balazinska

Clustering techniques are very attractive for extracting and identifying patterns in datasets. However, their application to very large spatial datasets presents numerous challenges such as high-dimensionality data, heterogeneity, and high…

Databases · Computer Science 2018-02-27 Malika Bendechache , Nhien-An Le-Khac , M-Tahar Kechadi