Search Results for author: Nan Wu

Found 44 papers, 13 papers with code

Zero-shot information extraction from radiological reports using ChatGPT

no code implementations4 Sep 2023 Danqing Hu, Bing Liu, Xiaofeng Zhu, Xudong Lu, Nan Wu

Information extraction is the strategy to transform the sequence of characters into structured data, which can be employed for secondary analysis.

Language Modelling Large Language Model +3

Air-Ground Integrated Sensing and Communications: Opportunities and Challenges

no code implementations13 Feb 2023 Zesong Fei, Xinyi Wang, Nan Wu, Jingxuan Huang, J. Andrew Zhang

The air-ground integrated sensing and communications (AG-ISAC) network, which consists of unmanned aerial vehicles (UAVs) and ground terrestrial networks, offers unique capabilities and demands special design techniques.

Privacy-Preserving Record Linkage for Cardinality Counting

no code implementations9 Jan 2023 Nan Wu, Dinusha Vatsalan, Mohamed Ali Kaafar, Sanath Kumar Ramesh

Several applications require counting the number of distinct items in the data, which is known as the cardinality counting problem.

Clustering Marketing +1

Heterogeneous Graph Tree Networks

1 code implementation1 Sep 2022 Nan Wu, Chaofan Wang

Heterogeneous graph neural networks (HGNNs) have attracted increasing research interest in recent three years.

Node Classification

Prediction of protein allosteric signalling pathways and functional residues through paths of optimised propensity

no code implementations14 Jul 2022 Nan Wu, Sophia N. Yaliraki, Mauricio Barahona

Key residues in both orthosteric and allosteric sites were identified and showed agreement with experimental results, and pivotal signalling residues along the pathway were also revealed, thus providing alternative targets for drug design.

Unsupervised Learning for Combinatorial Optimization with Principled Objective Relaxation

1 code implementation13 Jul 2022 Haoyu Wang, Nan Wu, Hang Yang, Cong Hao, Pan Li

Using machine learning to solve combinatorial optimization (CO) problems is challenging, especially when the data is unlabeled.

Combinatorial Optimization

Dog nose print matching with dual global descriptor based on Contrastive Learning

1 code implementation1 Jun 2022 Bin Li, Zhongan Wang, Nan Wu, Shuai Shi, Qijun Ma

These methods generally extract the global features as descriptor to represent the original image.

Contrastive Learning

Snake net and balloon force with a neural network for detecting multiple phases

no code implementations19 May 2022 Xiaodong Sun, Huijiong Yang, Nan Wu, T. C. Scott, Jie Zhang, Wanzhou Zhang

In order to obtain a physical phase diagram, the snake model with an artificial neural network is applied in an unsupervised learning way by the authors of [Phys. Rev. Lett.

GTNet: A Tree-Based Deep Graph Learning Architecture

1 code implementation27 Apr 2022 Nan Wu, Chaofan Wang

We formulate a general propagation rule following the nature of message passing in the tree to update a node's feature by aggregating its initial feature and its neighbor nodes' updated features.

Graph Attention Graph Learning +1

Characterizing and overcoming the greedy nature of learning in multi-modal deep neural networks

1 code implementation10 Feb 2022 Nan Wu, Stanisław Jastrzębski, Kyunghyun Cho, Krzysztof J. Geras

We propose an algorithm to balance the conditional learning speeds between modalities during training and demonstrate that it indeed addresses the issue of greedy learning.

LOSTIN: Logic Optimization via Spatio-Temporal Information with Hybrid Graph Models

1 code implementation20 Jan 2022 Nan Wu, Jiwon Lee, Yuan Xie, Cong Hao

Despite the stride made by machine learning (ML) based performance modeling, two major concerns that may impede production-ready ML applications in EDA are stringent accuracy requirements and generalization capability.

DeepMix: Mobility-aware, Lightweight, and Hybrid 3D Object Detection for Headsets

no code implementations15 Jan 2022 Yongjie Guan, Xueyu Hou, Nan Wu, Bo Han, Tao Han

In this paper, we propose DeepMix, a mobility-aware, lightweight, and hybrid 3D object detection framework for improving the user experience of AR/MR on mobile headsets.

2D Object Detection 3D Object Detection +2

Inferring Manifolds From Noisy Data Using Gaussian Processes

no code implementations14 Oct 2021 David B Dunson, Nan Wu

In analyzing complex datasets, it is often of interest to infer lower dimensional structure underlying the higher dimensional observations.

Gaussian Processes


no code implementations29 Sep 2021 Nan Wu, Chaofan Wang

Models adopting this scheme has the capability of going deep.

Graph Learning

Program-to-Circuit: Exploiting GNNs for Program Representation and Circuit Translation

no code implementations13 Sep 2021 Nan Wu, Huake He, Yuan Xie, Pan Li, Cong Hao

Pioneering in this direction, we expect more GNN endeavors to revolutionize this high-demand Program-to-Circuit problem and to enrich the expressiveness of GNNs on programs.

Transfer Learning Translation

Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosis

1 code implementation13 Jun 2021 Kangning Liu, Yiqiu Shen, Nan Wu, Jakub Chłędowski, Carlos Fernandez-Granda, Krzysztof J. Geras

In cancer diagnosis, interpretability can be achieved by localizing the region of the input image responsible for the output, i. e. the location of a lesion.

Medical Diagnosis Vocal Bursts Intensity Prediction +1

IronMan: GNN-assisted Design Space Exploration in High-Level Synthesis via Reinforcement Learning

no code implementations16 Feb 2021 Nan Wu, Yuan Xie, Cong Hao

Despite the great success of High-Level Synthesis (HLS) tools, we observe several unresolved challenges: 1) the high-level abstraction of programming styles in HLS sometimes conceals optimization opportunities; 2) existing HLS tools do not provide flexible trade-off (Pareto) solutions among different objectives and constraints; 3) the actual quality of the resulting RTL designs is hard to predict.

Reinforcement Learning (RL)

A Survey of Machine Learning for Computer Architecture and Systems

no code implementations16 Feb 2021 Nan Wu, Yuan Xie

Then, we summarize the common problems in computer architecture/system design that can be solved by ML techniques, and the typical ML techniques employed to resolve each of them.

BIG-bench Machine Learning Code Generation +1

Eigen-convergence of Gaussian kernelized graph Laplacian by manifold heat interpolation

1 code implementation25 Jan 2021 Xiuyuan Cheng, Nan Wu

The result holds for un-normalized and random-walk graph Laplacians when data are uniformly sampled on the manifold, as well as the density-corrected graph Laplacian (where the affinity matrix is normalized by the degree matrix from both sides) with non-uniformly sampled data.

Phoebe: Reuse-Aware Online Caching with Reinforcement Learning for Emerging Storage Models

no code implementations13 Nov 2020 Nan Wu, Pengcheng Li

With data durability, high access speed, low power efficiency and byte addressability, NVMe and SSD, which are acknowledged representatives of emerging storage technologies, have been applied broadly in many areas.

reinforcement-learning Reinforcement Learning (RL)

Graph Based Gaussian Processes on Restricted Domains

no code implementations14 Oct 2020 David B Dunson, Hau-Tieng Wu, Nan Wu

The GL is constructed from a kernel which depends only on the Euclidean coordinates of the inputs.

Gaussian Processes

Reducing false-positive biopsies with deep neural networks that utilize local and global information in screening mammograms

no code implementations19 Sep 2020 Nan Wu, Zhe Huang, Yiqiu Shen, Jungkyu Park, Jason Phang, Taro Makino, S. Gene Kim, Kyunghyun Cho, Laura Heacock, Linda Moy, Krzysztof J. Geras

Breast cancer is the most common cancer in women, and hundreds of thousands of unnecessary biopsies are done around the world at a tremendous cost.

An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department

1 code implementation4 Aug 2020 Farah E. Shamout, Yiqiu Shen, Nan Wu, Aakash Kaku, Jungkyu Park, Taro Makino, Stanisław Jastrzębski, Duo Wang, Ben Zhang, Siddhant Dogra, Meng Cao, Narges Razavian, David Kudlowitz, Lea Azour, William Moore, Yvonne W. Lui, Yindalon Aphinyanaphongs, Carlos Fernandez-Granda, Krzysztof J. Geras

In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time.

COVID-19 Diagnosis Decision Making +1

Strong Uniform Consistency with Rates for Kernel Density Estimators with General Kernels on Manifolds

no code implementations13 Jul 2020 Hau-Tieng Wu, Nan Wu

When analyzing modern machine learning algorithms, we may need to handle kernel density estimation (KDE) with intricate kernels that are not designed by the user and might even be irregular and asymmetric.

Density Estimation

Data-driven Efficient Solvers for Langevin Dynamics on Manifold in High Dimensions

no code implementations22 May 2020 Yuan Gao, Jian-Guo Liu, Nan Wu

To construct an efficient and stable approximation for the Langevin dynamics on $\mathcal{N}$, we leverage the corresponding Fokker-Planck equation on the manifold $\mathcal{N}$ in terms of the reaction coordinates $\mathsf{y}$.

The Cost of Privacy in Asynchronous Differentially-Private Machine Learning

no code implementations18 Mar 2020 Farhad Farokhi, Nan Wu, David Smith, Mohamed Ali Kaafar

The experiments illustrate that collaboration among more than 10 data owners with at least 10, 000 records with privacy budgets greater than or equal to 1 results in a superior machine-learning model in comparison to a model trained in isolation on only one of the datasets, illustrating the value of collaboration and the cost of the privacy.

BIG-bench Machine Learning Privacy Preserving

Memristor Hardware-Friendly Reinforcement Learning

no code implementations20 Jan 2020 Nan Wu, Adrien Vincent, Dmitri Strukov, Yuan Xie

Namely, neuromorphic architectures that leverage memristors, the programmable and nonvolatile two-terminal devices, as synaptic weights in hardware neural networks, are candidates of choice to realize such highly energy-efficient and complex nervous systems.

reinforcement-learning Reinforcement Learning (RL)

Improving localization-based approaches for breast cancer screening exam classification

no code implementations1 Aug 2019 Thibault Févry, Jason Phang, Nan Wu, S. Gene Kim, Linda Moy, Kyunghyun Cho, Krzysztof J. Geras

We trained and evaluated a localization-based deep CNN for breast cancer screening exam classification on over 200, 000 exams (over 1, 000, 000 images).

Classification General Classification

Screening Mammogram Classification with Prior Exams

no code implementations30 Jul 2019 Jungkyu Park, Jason Phang, Yiqiu Shen, Nan Wu, S. Gene Kim, Linda Moy, Kyunghyun Cho, Krzysztof J. Geras

Radiologists typically compare a patient's most recent breast cancer screening exam to their previous ones in making informed diagnoses.

Classification General Classification

The Value of Collaboration in Convex Machine Learning with Differential Privacy

no code implementations24 Jun 2019 Nan Wu, Farhad Farokhi, David Smith, Mohamed Ali Kaafar

In this paper, we apply machine learning to distributed private data owned by multiple data owners, entities with access to non-overlapping training datasets.

BIG-bench Machine Learning

When Locally Linear Embedding Hits Boundary

no code implementations11 Nov 2018 Hau-Tieng Wu, Nan Wu

Based on the Riemannian manifold model, we study the asymptotical behavior of a widely applied unsupervised learning algorithm, locally linear embedding (LLE), when the point cloud is sampled from a compact, smooth manifold with boundary.

Statistics Theory Statistics Theory 62-07

Network Modeling and Pathway Inference from Incomplete Data ("PathInf")

no code implementations1 Oct 2018 Xiang Li, Qitian Chen, Xing Wang, Ning Guo, Nan Wu, Quanzheng Li

In this work, we developed a network inference method from incomplete data ("PathInf") , as massive and non-uniformly distributed missing values is a common challenge in practical problems.

Data Summarization

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