1 code implementation • 29 Apr 2024 • Jun Yu, Yutong Dai, Xiaokang Liu, Jin Huang, Yishan Shen, Ke Zhang, Rong Zhou, Eashan Adhikarla, Wenxuan Ye, Yixin Liu, Zhaoming Kong, Kai Zhang, Yilong Yin, Vinod Namboodiri, Brian D. Davison, Jason H. Moore, Yong Chen
Overall, we hope this survey provides the research community with a comprehensive overview of the advancements in MTL from its inception in 1997 to the present in 2023.
1 code implementation • 29 Oct 2023 • Zhiling Yan, Kai Zhang, Rong Zhou, Lifang He, Xiang Li, Lichao Sun
In this paper, we critically evaluate the capabilities of the state-of-the-art multimodal large language model, i. e., GPT-4 with Vision (GPT-4V), on Visual Question Answering (VQA) task.
1 code implementation • 26 May 2023 • Kai Zhang, Jun Yu, Eashan Adhikarla, Rong Zhou, Zhiling Yan, Yixin Liu, Zhengliang Liu, Lifang He, Brian Davison, Xiang Li, Hui Ren, Sunyang Fu, James Zou, Wei Liu, Jing Huang, Chen Chen, Yuyin Zhou, Tianming Liu, Xun Chen, Yong Chen, Quanzheng Li, Hongfang Liu, Lichao Sun
Conventional task- and modality-specific artificial intelligence (AI) models are inflexible in real-world deployment and maintenance for biomedicine.
Ranked #1 on Text Summarization on MeQSum
no code implementations • 13 Nov 2022 • Xuetong Wang, Kanhao Zhao, Rong Zhou, Alex Leow, Ricardo Osorio, Yu Zhang, Lifang He
Normative modeling is an emerging and promising approach to effectively study disorder heterogeneity in individual participants.
1 code implementation • 6 Jul 2022 • Yuemeng Li, Miguel Romanello Joaquim, Stephen Pickup, Hee Kwon Song, Rong Zhou, Yong Fan
Purpose: To accelerate radially sampled diffusion weighted spin-echo (Rad-DW-SE) acquisition method for generating high quality apparent diffusion coefficient (ADC) maps.
no code implementations • 14 Oct 2020 • Yiren Chen, Yaming Yang, Hong Sun, Yujing Wang, Yu Xu, Wei Shen, Rong Zhou, Yunhai Tong, Jing Bai, Ruofei Zhang
We add the model designed by AutoADR as a sub-model into the production Ad Relevance model.
2 code implementations • IJCAI 2018 • Nesreen K. Ahmed, Ryan Rossi, John Boaz Lee, Theodore L. Willke, Rong Zhou, Xiangnan Kong, Hoda Eldardiry
Random walks are at the heart of many existing network embedding methods.
no code implementations • 27 Oct 2017 • Ryan A. Rossi, Nesreen K. Ahmed, Hoda Eldardiry, Rong Zhou
Multi-label classification is an important learning problem with many applications.
no code implementations • 25 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 $w$.
no code implementations • 14 Sep 2017 • Nesreen K. Ahmed, Ryan A. Rossi, Rong Zhou, John Boaz Lee, Xiangnan Kong, Theodore L. Willke, Hoda Eldardiry
Random walks are at the heart of many existing deep learning algorithms for graph data.
no code implementations • 28 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.
no code implementations • 6 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.
no code implementations • 4 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.
no code implementations • 18 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).
no code implementations • 2 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.
no code implementations • 16 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.
no code implementations • 16 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.