Search Results for author: Rong Zhou

Found 14 papers, 1 papers with code

Learning Apparent Diffusion Coefficient Maps from Undersampled Radial k-Space Diffusion-Weighted MRI in Mice using a Deep CNN-Transformer Model in Conjunction with a Monoexponential Model

no code implementations6 Jul 2022 Yuemeng Li, Hee Kwon Song, Miguel Romanello Joaquim, Stephen Pickup, Rong Zhou, Yong Fan

Purpose: To accelerate radially sampled diffusion weighted spin-echo (Rad-DW-SE) acquisition method for generating high quality of apparent diffusion coefficient (ADC) maps.

Inductive Representation Learning in Large Attributed Graphs

no code implementations25 Oct 2017 Nesreen K. Ahmed, Ryan A. Rossi, Rong Zhou, John Boaz Lee, Xiangnan Kong, Theodore L. Willke, Hoda Eldardiry

To make these methods more generally applicable, we propose a framework for inductive network representation learning based on the notion of attributed random walk that is not tied to node identity and is instead based on learning a function $\Phi : \mathrm{\rm \bf x} \rightarrow w$ that maps a node attribute vector $\mathrm{\rm \bf x}$ to a type $w$.

Anomaly Detection Link Prediction +1

Deep Feature Learning for Graphs

no code implementations28 Apr 2017 Ryan A. Rossi, Rong Zhou, Nesreen K. Ahmed

This paper presents a general graph representation learning framework called DeepGL for learning deep node and edge representations from large (attributed) graphs.

Graph Representation Learning Transfer Learning

Estimation of Graphlet Statistics

no code implementations6 Jan 2017 Ryan A. Rossi, Rong Zhou, Nesreen K. Ahmed

In this work, we propose an unbiased graphlet estimation framework that is (a) fast with significant speedups compared to the state-of-the-art, (b) parallel with nearly linear-speedups, (c) accurate with <1% relative error, (d) scalable and space-efficient for massive networks with billions of edges, and (e) flexible for a variety of real-world settings, as well as estimating macro and micro-level graphlet statistics (e. g., counts) of both connected and disconnected graphlets.

Revisiting Role Discovery in Networks: From Node to Edge Roles

no code implementations4 Oct 2016 Nesreen K. Ahmed, Ryan A. Rossi, Theodore L. Willke, Rong Zhou

The experimental results demonstrate the utility of edge roles for network analysis tasks on a variety of graphs from various problem domains.

Hybrid CPU-GPU Framework for Network Motifs

no code implementations18 Aug 2016 Ryan A. Rossi, Rong Zhou

In this paper, we propose a key class of hybrid parallel graphlet algorithms that leverages multiple CPUs and GPUs simultaneously for computing k-vertex induced subgraph statistics (called graphlets).

Relational Similarity Machines

no code implementations2 Aug 2016 Ryan A. Rossi, Rong Zhou, Nesreen K. Ahmed

Despite the importance of relational learning, most existing methods are hard to adapt to different settings, due to issues with efficiency, scalability, accuracy, and flexibility for handling a wide variety of classification problems, data, constraints, and tasks.

General Classification Multi-class Classification +1

On-line Planning and Scheduling: An Application to Controlling Modular Printers

no code implementations16 Jan 2014 Wheeler Ruml, Minh Binh Do, Rong Zhou, Markus P. J. Fromherz

To our knowledge, this work represents the first successful industrial application of embedded domain-independent temporal planning.

Decision Making Scheduling

Best-First Heuristic Search for Multicore Machines

no code implementations16 Jan 2014 Ethan Burns, Sofia Lemons, Wheeler Ruml, Rong Zhou

To harness modern multicore processors, it is imperative to develop parallel versions of fundamental algorithms.

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