Search Results for author: Yifan Hu

Found 45 papers, 14 papers with code

TNT: Text Normalization based Pre-training of Transformers for Content Moderation

no code implementations EMNLP 2020 Fei Tan, Yifan Hu, Changwei Hu, Keqian Li, Kevin Yen

In this work, we present a new language pre-training model TNT (Text Normalization based pre-training of Transformers) for content moderation.

Distributionally Robust Model-based Reinforcement Learning with Large State Spaces

no code implementations5 Sep 2023 Shyam Sundhar Ramesh, Pier Giuseppe Sessa, Yifan Hu, Andreas Krause, Ilija Bogunovic

Three major challenges in reinforcement learning are the complex dynamical systems with large state spaces, the costly data acquisition processes, and the deviation of real-world dynamics from the training environment deployment.

Gaussian Processes Model-based Reinforcement Learning +1

Deep Directly-Trained Spiking Neural Networks for Object Detection

1 code implementation ICCV 2023 Qiaoyi Su, Yuhong Chou, Yifan Hu, Jianing Li, Shijie Mei, Ziyang Zhang, Guoqi Li

Spiking neural networks (SNNs) are brain-inspired energy-efficient models that encode information in spatiotemporal dynamics.

object-detection Object Detection

CRYPTEXT: Database and Interactive Toolkit of Human-Written Text Perturbations in the Wild

no code implementations16 Jan 2023 Thai Le, Ye Yiran, Yifan Hu, Dongwon Lee

CRYPTEXT is a data-intensive application that provides the users with a database and several tools to extract and interact with human-written perturbations.

Deep-Reinforcement-Learning-based Path Planning for Industrial Robots using Distance Sensors as Observation

1 code implementation14 Jan 2023 Teham Bhuiyan, Linh Kästner, Yifan Hu, Benno Kutschank, Jens Lambrecht

Industrial robots are widely used in various manufacturing environments due to their efficiency in doing repetitive tasks such as assembly or welding.

Industrial Robots Motion Planning +2

MnTTS2: An Open-Source Multi-Speaker Mongolian Text-to-Speech Synthesis Dataset

1 code implementation11 Dec 2022 Kailin Liang, Bin Liu, Yifan Hu, Rui Liu, Feilong Bao, Guanglai Gao

Text-to-Speech (TTS) synthesis for low-resource languages is an attractive research issue in academia and industry nowadays.

Speech Synthesis Text-To-Speech Synthesis

FCTalker: Fine and Coarse Grained Context Modeling for Expressive Conversational Speech Synthesis

1 code implementation27 Oct 2022 Yifan Hu, Rui Liu, Guanglai Gao, Haizhou Li

Therefore, we propose a novel expressive conversational TTS model, termed as FCTalker, that learn the fine and coarse grained context dependency at the same time during speech generation.

Speech Synthesis

Attention Spiking Neural Networks

no code implementations28 Sep 2022 Man Yao, Guangshe Zhao, Hengyu Zhang, Yifan Hu, Lei Deng, Yonghong Tian, Bo Xu, Guoqi Li

On ImageNet-1K, we achieve top-1 accuracy of 75. 92% and 77. 08% on single/4-step Res-SNN-104, which are state-of-the-art results in SNNs.

Action Recognition Image Classification

MnTTS: An Open-Source Mongolian Text-to-Speech Synthesis Dataset and Accompanied Baseline

1 code implementation22 Sep 2022 Yifan Hu, Pengkai Yin, Rui Liu, Feilong Bao, Guanglai Gao

This paper introduces a high-quality open-source text-to-speech (TTS) synthesis dataset for Mongolian, a low-resource language spoken by over 10 million people worldwide.

Speech Synthesis Text-To-Speech Synthesis

BSDGAN: Balancing Sensor Data Generative Adversarial Networks for Human Activity Recognition

no code implementations7 Aug 2022 Yifan Hu, Yu Wang

However, due to the inconsistent frequency of human activities, the amount of data for each activity in the human activity dataset is imbalanced.

Human Activity Recognition

SmartGD: A GAN-Based Graph Drawing Framework for Diverse Aesthetic Goals

no code implementations13 Jun 2022 Xiaoqi Wang, Kevin Yen, Yifan Hu, Han-Wei Shen

There are a few existing methods that have attempted to develop a flexible solution for optimizing different aesthetic aspects measured by different aesthetic criteria.

Generalization Bounds of Nonconvex-(Strongly)-Concave Stochastic Minimax Optimization

no code implementations28 May 2022 Siqi Zhang, Yifan Hu, Liang Zhang, Niao He

We further study the algorithm-dependent generalization bounds via stability arguments of algorithms.

Generalization Bounds

Perturbations in the Wild: Leveraging Human-Written Text Perturbations for Realistic Adversarial Attack and Defense

1 code implementation Findings (ACL) 2022 Thai Le, Jooyoung Lee, Kevin Yen, Yifan Hu, Dongwon Lee

We find that adversarial texts generated by ANTHRO achieve the best trade-off between (1) attack success rate, (2) semantic preservation of the original text, and (3) stealthiness--i. e. indistinguishable from human writings hence harder to be flagged as suspicious.

Adversarial Attack

SuperCone: Unified User Segmentation over Heterogeneous Experts via Concept Meta-learning

no code implementations9 Mar 2022 Keqian Li, Yifan Hu

We study the problem of user segmentation: given a set of users and one or more predefined groups or segments, assign users to their corresponding segments.


MetaCon: Unified Predictive Segments System with Trillion Concept Meta-Learning

no code implementations9 Mar 2022 Keqian Li, Yifan Hu, Logan Palanisamy, Lisa Jones, Akshay Gupta, Jason Grigsby, Ili Selinger, Matt Gillingham, Fei Tan

Accurate understanding of users in terms of predicative segments play an essential role in the day to day operation of modern internet enterprises.


Rethinking Pretraining as a Bridge from ANNs to SNNs

1 code implementation2 Mar 2022 Yihan Lin, Yifan Hu, Shijie Ma, Guoqi Li, Dongjie Yu

In this work, a new SNN training paradigm is proposed by combining the concepts of the two different training methods with the help of the pretrain technique and BP-based deep SNN training mechanism.

Advancing Spiking Neural Networks towards Deep Residual Learning

1 code implementation15 Dec 2021 Yifan Hu, Lei Deng, Yujie Wu, Man Yao, Guoqi Li

Despite the rapid progress of neuromorphic computing, inadequate capacity and insufficient representation power of spiking neural networks (SNNs) severely restrict their application scope in practice.

Advancing Deep Residual Learning by Solving the Crux of Degradation in Spiking Neural Networks

no code implementations9 Dec 2021 Yifan Hu, Yujie Wu, Lei Deng, Guoqi Li

In this paper, we identify the crux and then propose a novel residual block for SNNs, which is able to significantly extend the depth of directly trained SNNs, e. g., up to 482 layers on CIFAR-10 and 104 layers on ImageNet, without observing any slight degradation problem.

On the Bias-Variance-Cost Tradeoff of Stochastic Optimization

no code implementations NeurIPS 2021 Yifan Hu, Xin Chen, Niao He

We consider stochastic optimization when one only has access to biased stochastic oracles of the objective, and obtaining stochastic gradients with low biases comes at high costs.

Bilevel Optimization Stochastic Optimization

BERT-Beta: A Proactive Probabilistic Approach to Text Moderation

no code implementations EMNLP 2021 Fei Tan, Yifan Hu, Kevin Yen, Changwei Hu

Text moderation for user generated content, which helps to promote healthy interaction among users, has been widely studied and many machine learning models have been proposed.


TSI: an Ad Text Strength Indicator using Text-to-CTR and Semantic-Ad-Similarity

no code implementations18 Aug 2021 Shaunak Mishra, Changwei Hu, Manisha Verma, Kevin Yen, Yifan Hu, Maxim Sviridenko

To realize this opportunity, we propose an ad text strength indicator (TSI) which: (i) predicts the click-through-rate (CTR) for an input ad text, (ii) fetches similar existing ads to create a neighborhood around the input ad, (iii) and compares the predicted CTRs in the neighborhood to declare whether the input ad is strong or weak.

Click-Through Rate Prediction Retrieval +1

DeepGD: A Deep Learning Framework for Graph Drawing Using GNN

no code implementations27 Jun 2021 Xiaoqi Wang, Kevin Yen, Yifan Hu, Han-Wei Shen

In this paper, we propose a Convolutional Graph Neural Network based deep learning framework, DeepGD, which can draw arbitrary graphs once trained.

Efficient Ring-topology Decentralized Federated Learning with Deep Generative Models for Industrial Artificial Intelligent

no code implementations15 Apr 2021 Zhao Wang, Yifan Hu, Jun Xiao, Chao Wu

A novel ring FL topology as well as a map-reduce based synchronizing method are designed in the proposed RDFL to improve decentralized FL performance and bandwidth utilization.

Federated Learning

Political Posters Identification with Appearance-Text Fusion

no code implementations19 Dec 2020 Xuan Qin, Meizhu Liu, Yifan Hu, Christina Moo, Christian M. Riblet, Changwei Hu, Kevin Yen, Haibin Ling

In this paper, we propose a method that efficiently utilizes appearance features and text vectors to accurately classify political posters from other similar political images.

Going Deeper With Directly-Trained Larger Spiking Neural Networks

2 code implementations29 Oct 2020 Hanle Zheng, Yujie Wu, Lei Deng, Yifan Hu, Guoqi Li

To this end, we propose a threshold-dependent batch normalization (tdBN) method based on the emerging spatio-temporal backpropagation, termed "STBP-tdBN", enabling direct training of a very deep SNN and the efficient implementation of its inference on neuromorphic hardware.

GFL: A Decentralized Federated Learning Framework Based On Blockchain

no code implementations21 Oct 2020 Yifan Hu, YuHang Zhou, Jun Xiao, Chao Wu

Federated learning(FL) is a rapidly growing field and many centralized and decentralized FL frameworks have been proposed.

Data Poisoning Federated Learning

HABERTOR: An Efficient and Effective Deep Hatespeech Detector

no code implementations EMNLP 2020 Thanh Tran, Yifan Hu, Changwei Hu, Kevin Yen, Fei Tan, Kyumin Lee, Serim Park

HABERTOR inherits BERT's architecture, but is different in four aspects: (i) it generates its own vocabularies and is pre-trained from the scratch using the largest scale hatespeech dataset; (ii) it consists of Quaternion-based factorized components, resulting in a much smaller number of parameters, faster training and inferencing, as well as less memory usage; (iii) it uses our proposed multi-source ensemble heads with a pooling layer for separate input sources, to further enhance its effectiveness; and (iv) it uses a regularized adversarial training with our proposed fine-grained and adaptive noise magnitude to enhance its robustness.

Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs By Comparing Image Representations

1 code implementation15 Jul 2020 Hong-Yu Zhou, Shuang Yu, Cheng Bian, Yifan Hu, Kai Ma, Yefeng Zheng

In deep learning era, pretrained models play an important role in medical image analysis, in which ImageNet pretraining has been widely adopted as the best way.

Embedding Task Knowledge into 3D Neural Networks via Self-supervised Learning

no code implementations10 Jun 2020 Jiuwen Zhu, Yuexiang Li, Yifan Hu, S. Kevin Zhou

To this end, self-supervised learning (SSL), as a potential solution for deficient annotated data, attracts increasing attentions from the community.

Clustering General Classification +3

Multi-Modality Generative Adversarial Networks with Tumor Consistency Loss for Brain MR Image Synthesis

1 code implementation2 May 2020 Bingyu Xin, Yifan Hu, Yefeng Zheng, Hongen Liao

We use the synthesized modalities by TC-MGAN to boost the tumor segmentation accuracy, and the results demonstrate its effectiveness.

Image Generation Tumor Segmentation

Biased Stochastic Gradient Descent for Conditional Stochastic Optimization

no code implementations25 Feb 2020 Yifan Hu, Siqi Zhang, Xin Chen, Niao He

Conditional Stochastic Optimization (CSO) covers a variety of applications ranging from meta-learning and causal inference to invariant learning.

Causal Inference Meta-Learning +2

Large-scale Gender/Age Prediction of Tumblr Users

no code implementations2 Jan 2020 Yao Zhan, Changwei Hu, Yifan Hu, Tejaswi Kasturi, Shanmugam Ramasamy, Matt Gillingham, Keith Yamamoto

In this paper, we propose graph based and deep learning models for age and gender predictions, which take into account user activities and content features.

Network Embedding

A Deep Structural Model for Analyzing Correlated Multivariate Time Series

no code implementations2 Jan 2020 Changwei Hu, Yifan Hu, Sungyong Seo

The seasonality component is approximated via a non-liner function of a set of Fourier terms, and the event components are learned by a simple linear function of regressor encoding the event dates.

Time Series Time Series Analysis

Comprehensive SNN Compression Using ADMM Optimization and Activity Regularization

no code implementations3 Nov 2019 Lei Deng, Yujie Wu, Yifan Hu, Ling Liang, Guoqi Li, Xing Hu, Yufei Ding, Peng Li, Yuan Xie

As well known, the huge memory and compute costs of both artificial neural networks (ANNs) and spiking neural networks (SNNs) greatly hinder their deployment on edge devices with high efficiency.

Model Compression Quantization

Self-supervised Feature Learning for 3D Medical Images by Playing a Rubik's Cube

no code implementations5 Oct 2019 Xinrui Zhuang, Yuexiang Li, Yifan Hu, Kai Ma, Yujiu Yang, Yefeng Zheng

Witnessed the development of deep learning, increasing number of studies try to build computer aided diagnosis systems for 3D volumetric medical data.

Brain Tumor Segmentation Rubik's Cube +2

A GLCM Embedded CNN Strategy for Computer-aided Diagnosis in Intracerebral Hemorrhage

no code implementations5 Jun 2019 Yifan Hu, Yefeng Zheng

Computer-aided diagnosis (CADx) systems have been shown to assist radiologists by providing classifications of all kinds of medical images like Computed tomography (CT) and Magnetic resonance (MR).

Computed Tomography (CT) General Classification

Sample Complexity of Sample Average Approximation for Conditional Stochastic Optimization

no code implementations28 May 2019 Yifan Hu, Xin Chen, Niao He

In this paper, we study a class of stochastic optimization problems, referred to as the \emph{Conditional Stochastic Optimization} (CSO), in the form of $\min_{x \in \mathcal{X}} \EE_{\xi}f_\xi\Big({\EE_{\eta|\xi}[g_\eta(x,\xi)]}\Big)$, which finds a wide spectrum of applications including portfolio selection, reinforcement learning, robust learning, causal inference and so on.

Causal Inference Stochastic Optimization

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

1 code implementation5 Nov 2018 Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara, Christoph Berger, Sung Min Ha, Martin Rozycki, Marcel Prastawa, Esther Alberts, Jana Lipkova, John Freymann, Justin Kirby, Michel Bilello, Hassan Fathallah-Shaykh, Roland Wiest, Jan Kirschke, Benedikt Wiestler, Rivka Colen, Aikaterini Kotrotsou, Pamela Lamontagne, Daniel Marcus, Mikhail Milchenko, Arash Nazeri, Marc-Andre Weber, Abhishek Mahajan, Ujjwal Baid, Elizabeth Gerstner, Dongjin Kwon, Gagan Acharya, Manu Agarwal, Mahbubul Alam, Alberto Albiol, Antonio Albiol, Francisco J. Albiol, Varghese Alex, Nigel Allinson, Pedro H. A. Amorim, Abhijit Amrutkar, Ganesh Anand, Simon Andermatt, Tal Arbel, Pablo Arbelaez, Aaron Avery, Muneeza Azmat, Pranjal B., W Bai, Subhashis Banerjee, Bill Barth, Thomas Batchelder, Kayhan Batmanghelich, Enzo Battistella, Andrew Beers, Mikhail Belyaev, Martin Bendszus, Eze Benson, Jose Bernal, Halandur Nagaraja Bharath, George Biros, Sotirios Bisdas, James Brown, Mariano Cabezas, Shilei Cao, Jorge M. Cardoso, Eric N Carver, Adrià Casamitjana, Laura Silvana Castillo, Marcel Catà, Philippe Cattin, Albert Cerigues, Vinicius S. Chagas, Siddhartha Chandra, Yi-Ju Chang, Shiyu Chang, Ken Chang, Joseph Chazalon, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Chen, Kun Cheng, Ahana Roy Choudhury, Roger Chylla, Albert Clérigues, Steven Colleman, Ramiro German Rodriguez Colmeiro, Marc Combalia, Anthony Costa, Xiaomeng Cui, Zhenzhen Dai, Lutao Dai, Laura Alexandra Daza, Eric Deutsch, Changxing Ding, Chao Dong, Shidu Dong, Wojciech Dudzik, Zach Eaton-Rosen, Gary Egan, Guilherme Escudero, Théo Estienne, Richard Everson, Jonathan Fabrizio, Yong Fan, Longwei Fang, Xue Feng, Enzo Ferrante, Lucas Fidon, Martin Fischer, Andrew P. French, Naomi Fridman, Huan Fu, David Fuentes, Yaozong Gao, Evan Gates, David Gering, Amir Gholami, Willi Gierke, Ben Glocker, Mingming Gong, Sandra González-Villá, T. Grosges, Yuanfang Guan, Sheng Guo, Sudeep Gupta, Woo-Sup Han, Il Song Han, Konstantin Harmuth, Huiguang He, Aura Hernández-Sabaté, Evelyn Herrmann, Naveen Himthani, Winston Hsu, Cheyu Hsu, Xiaojun Hu, Xiaobin Hu, Yan Hu, Yifan Hu, Rui Hua, Teng-Yi Huang, Weilin Huang, Sabine Van Huffel, Quan Huo, Vivek HV, Khan M. Iftekharuddin, Fabian Isensee, Mobarakol Islam, Aaron S. Jackson, Sachin R. Jambawalikar, Andrew Jesson, Weijian Jian, Peter Jin, V Jeya Maria Jose, Alain Jungo, B Kainz, Konstantinos Kamnitsas, Po-Yu Kao, Ayush Karnawat, Thomas Kellermeier, Adel Kermi, Kurt Keutzer, Mohamed Tarek Khadir, Mahendra Khened, Philipp Kickingereder, Geena Kim, Nik King, Haley Knapp, Urspeter Knecht, Lisa Kohli, Deren Kong, Xiangmao Kong, Simon Koppers, Avinash Kori, Ganapathy Krishnamurthi, Egor Krivov, Piyush Kumar, Kaisar Kushibar, Dmitrii Lachinov, Tryphon Lambrou, Joon Lee, Chengen Lee, Yuehchou Lee, M Lee, Szidonia Lefkovits, Laszlo Lefkovits, James Levitt, Tengfei Li, Hongwei Li, Hongyang Li, Xiaochuan Li, Yuexiang Li, Heng Li, Zhenye Li, Xiaoyu Li, Zeju Li, Xiaogang Li, Wenqi Li, Zheng-Shen Lin, Fengming Lin, Pietro Lio, Chang Liu, Boqiang Liu, Xiang Liu, Mingyuan Liu, Ju Liu, Luyan Liu, Xavier Llado, Marc Moreno Lopez, Pablo Ribalta Lorenzo, Zhentai Lu, Lin Luo, Zhigang Luo, Jun Ma, Kai Ma, Thomas Mackie, Anant Madabushi, Issam Mahmoudi, Klaus H. Maier-Hein, Pradipta Maji, CP Mammen, Andreas Mang, B. S. Manjunath, Michal Marcinkiewicz, S McDonagh, Stephen McKenna, Richard McKinley, Miriam Mehl, Sachin Mehta, Raghav Mehta, Raphael Meier, Christoph Meinel, Dorit Merhof, Craig Meyer, Robert Miller, Sushmita Mitra, Aliasgar Moiyadi, David Molina-Garcia, Miguel A. B. Monteiro, Grzegorz Mrukwa, Andriy Myronenko, Jakub Nalepa, Thuyen Ngo, Dong Nie, Holly Ning, Chen Niu, Nicholas K Nuechterlein, Eric Oermann, Arlindo Oliveira, Diego D. C. Oliveira, Arnau Oliver, Alexander F. I. Osman, Yu-Nian Ou, Sebastien Ourselin, Nikos Paragios, Moo Sung Park, Brad Paschke, J. Gregory Pauloski, Kamlesh Pawar, Nick Pawlowski, Linmin Pei, Suting Peng, Silvio M. Pereira, Julian Perez-Beteta, Victor M. Perez-Garcia, Simon Pezold, Bao Pham, Ashish Phophalia, Gemma Piella, G. N. Pillai, Marie Piraud, Maxim Pisov, Anmol Popli, Michael P. Pound, Reza Pourreza, Prateek Prasanna, Vesna Prkovska, Tony P. Pridmore, Santi Puch, Élodie Puybareau, Buyue Qian, Xu Qiao, Martin Rajchl, Swapnil Rane, Michael Rebsamen, Hongliang Ren, Xuhua Ren, Karthik Revanuru, Mina Rezaei, Oliver Rippel, Luis Carlos Rivera, Charlotte Robert, Bruce Rosen, Daniel Rueckert, Mohammed Safwan, Mostafa Salem, Joaquim Salvi, Irina Sanchez, Irina Sánchez, Heitor M. Santos, Emmett Sartor, Dawid Schellingerhout, Klaudius Scheufele, Matthew R. Scott, Artur A. Scussel, Sara Sedlar, Juan Pablo Serrano-Rubio, N. Jon Shah, Nameetha Shah, Mazhar Shaikh, B. Uma Shankar, Zeina Shboul, Haipeng Shen, Dinggang Shen, Linlin Shen, Haocheng Shen, Varun Shenoy, Feng Shi, Hyung Eun Shin, Hai Shu, Diana Sima, M Sinclair, Orjan Smedby, James M. Snyder, Mohammadreza Soltaninejad, Guidong Song, Mehul Soni, Jean Stawiaski, Shashank Subramanian, Li Sun, Roger Sun, Jiawei Sun, Kay Sun, Yu Sun, Guoxia Sun, Shuang Sun, Yannick R Suter, Laszlo Szilagyi, Sanjay Talbar, DaCheng Tao, Zhongzhao Teng, Siddhesh Thakur, Meenakshi H Thakur, Sameer Tharakan, Pallavi Tiwari, Guillaume Tochon, Tuan Tran, Yuhsiang M. Tsai, Kuan-Lun Tseng, Tran Anh Tuan, Vadim Turlapov, Nicholas Tustison, Maria Vakalopoulou, Sergi Valverde, Rami Vanguri, Evgeny Vasiliev, Jonathan Ventura, Luis Vera, Tom Vercauteren, C. A. Verrastro, Lasitha Vidyaratne, Veronica Vilaplana, Ajeet Vivekanandan, Qian Wang, Chiatse J. Wang, Wei-Chung Wang, Duo Wang, Ruixuan Wang, Yuanyuan Wang, Chunliang Wang, Guotai Wang, Ning Wen, Xin Wen, Leon Weninger, Wolfgang Wick, Shaocheng Wu, Qiang Wu, Yihong Wu, Yong Xia, Yanwu Xu, Xiaowen Xu, Peiyuan Xu, Tsai-Ling Yang, Xiaoping Yang, Hao-Yu Yang, Junlin Yang, Haojin Yang, Guang Yang, Hongdou Yao, Xujiong Ye, Changchang Yin, Brett Young-Moxon, Jinhua Yu, Xiangyu Yue, Songtao Zhang, Angela Zhang, Kun Zhang, Xue-jie Zhang, Lichi Zhang, Xiaoyue Zhang, Yazhuo Zhang, Lei Zhang, Jian-Guo Zhang, Xiang Zhang, Tianhao Zhang, Sicheng Zhao, Yu Zhao, Xiaomei Zhao, Liang Zhao, Yefeng Zheng, Liming Zhong, Chenhong Zhou, Xiaobing Zhou, Fan Zhou, Hongtu Zhu, Jin Zhu, Ying Zhuge, Weiwei Zong, Jayashree Kalpathy-Cramer, Keyvan Farahani, Christos Davatzikos, Koen van Leemput, Bjoern Menze

This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.

Brain Tumor Segmentation Survival Prediction +1

Nationality Classification Using Name Embeddings

1 code implementation25 Aug 2017 Junting Ye, Shuchu Han, Yifan Hu, Baris Coskun, Meizhu Liu, Hong Qin, Steven Skiena

Through our analysis of 57M contact lists from a major Internet company, we are able to design a fine-grained nationality classifier covering 39 groups representing over 90% of the world population.

Classification General Classification

HARP: Hierarchical Representation Learning for Networks

3 code implementations23 Jun 2017 Haochen Chen, Bryan Perozzi, Yifan Hu, Steven Skiena

We present HARP, a novel method for learning low dimensional embeddings of a graph's nodes which preserves higher-order structural features.

Social and Information Networks

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