Scalable Parallel Topic Models

Scalable Inference for Logistic-Normal Topic Models

Logistic-normal topic models can effectively discover correlation structures among latent topics. However, their inference remains a challenge because of the non-conjugacy between the logistic-normal prior and multinomial topic mixing proportions. Existing algorithms either make restricting mean-field assumptions or are not scalable to large-scale applications. This paper presents a partially c...

Models, Inference, and Implementation for Scalable Probabilistic Models of Text

Title of dissertation: Models, Inference, and Implementation for Scalable Probabilistic Models of Text Ke Zhai, Ph.D., 2014 Dept. of Computer Science Dissertation directed by: Professor Jordan Boyd-Graber iSchool, UMIACS Unsupervised probabilistic Bayesian models are powerful tools for statistical analysis, especially in the area of information retrieval, document analysis and text processing. ...

Scalable Parallel Computing on Clouds: Efficient and Scalable Architectures to Perform Pleasingly Parallel, Mapreduce and Iterative Data Intensive Computations on Cloud Environments

Scalable Parellel Octree Using HPX With Hilbert Curve

and D. Fey, “Hpx – a task based programming model in a global address space,” PGAS 2014: The 8th International Conference on Partitioned Global Address Space Programming Models, 2014. [2] Zahra Khatami, Hartmut Kaiser, Patricia Grubel, Bryce Adelstein-Lelbach, Adrian Serio and J. Ramanujam, “A massively parallel distributed N-Body simulation code implemented with HPX”, 28th ACM Symposium on Par...

Parallelization of Rich Models for Steganalysis of Digital Images using a CUDA-based Approach

There are several different methods to make an efficient strategy for steganalysis of digital images. A very powerful method in this area is rich model consisting of a large number of diverse sub-models in both spatial and transform domain that should be utilized. However, the extraction of a various types of features from an image is so time consuming in some steps, especially for training pha...