Dynamic Parameter Reallocation Improves Trainability of Deep Convolutional Networks

Network pruning has emerged as a powerful technique for reducing the size of deep neural networks. Pruning uncovers high-performance subnetworks by taking a trained dense network and gradually removing unimportant connections. Recently, alternative techniques have emerged for training sparse networks directly without having to train a large dense model beforehand, thereby achieving small memory footprints…

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Temporo-Spatial Collaborative Filtering for Parameter Estimation in Noisy DCE-MRI Sequences: Application to Breast Cancer Chemotherapy Response

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a minimally invasive imaging technique which can be used for characterizing tumor biology and tumor response to radiotherapy. Pharmacokinetic (PK) estimation is widely used for DCE-MRI data analysis to extract quantitative parameters relating to microvasculature characteristics of the cancerous tissues. Unavoidable noise corruption during DCE-MRI data acquisition has…

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Real-Time Full Correlation Matrix Analysis of fMRI Data

Real-time functional magnetic resonance imaging (rtfMRI) is an emerging approach for studying the functioning of the human brain. Computational challenges combined with high data velocity have to this point restricted rtfMRI analyses to studying regions of the brain independently. However, given neural processing is accomplished via functional interactions among brain regions, neuroscience could stand to…

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  • Tony C. Pan
  • Sanchit Misra
  • Srinivas Aluru

Optimizing High Performance Distributed Memory Parallel Hash Tables for DNA k-mer Counting

High-throughput DNA sequencing is the mainstay of modern genomics research. A common operation used in bioinformatic analysis for many applications of high-throughput sequencing is the counting and indexing of fixed length substrings of DNA sequences called k-mers. Counting k-mers is often ac- complished via hashing, and distributed memory k-mer counting algorithms for large datasets are…

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Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search

We present a learning-based approach to computing solutions for certain NP-hard problems. Our approach combines deep learning techniques with useful algorithmic elements from classic heuristics. The central component is a graph convolutional network that is trained to estimate the likelihood, for each vertex in a graph, of whether this vertex is part of the optimal…

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Sparse DNNs with Improved Adversarial Robustness

Deep neural networks (DNNs) are computationally/memory-intensive and vulnerable to adversarial attacks, making them prohibitive in some real-world applications. By converting dense models into sparse ones, pruning appears to be a promising solution to reducing the computation/memory cost. This paper studies classification models, especially DNN-based ones, to demonstrate that there exists intrinsic relationships between their sparsity…

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  • Stijn Eyerman
  • Wim Heirman
  • Kristof Du Bois
  • Joshua B. Fryman
  • Ibrahim Hur

Many-Core Graph Workload Analysis

Graph applications have specific characteristics that are not common in other application domains and therefore require thorough analysis to guide future graph processing hardware design. In this paper, we analyze multiple graph applications on current multi and many-core processors, and provide conclusions and recommendations for future designs. We restate well-known characteristics of graph applications, such…

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Towards Understanding End-of-trip Instructions in a Taxi Ride Scenario

We introduce a dataset containing human-authored descriptions of target locations in an "end-of-trip in a taxi ride" scenario. We describe our data collection method and a novel annotation scheme that supports understanding of such descriptions of target locations. Our dataset contains target location descriptions for both synthetic and real-world images as well as visual annotations…

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