Search Results for author: Xia Hu

Found 82 papers, 23 papers with code

Orthogonal Graph Neural Networks

no code implementations23 Sep 2021 Kai Guo, Kaixiong Zhou, Xia Hu, Yu Li, Yi Chang, Xin Wang

Graph neural networks (GNNs) have received tremendous attention due to their superiority in learning node representations.

Adaptive Label Smoothing To Regularize Large-Scale Graph Training

no code implementations30 Aug 2021 Kaixiong Zhou, Ninghao Liu, Fan Yang, Zirui Liu, Rui Chen, Li Li, Soo-Hyun Choi, Xia Hu

Graph neural networks (GNNs), which learn the node representations by recursively aggregating information from its neighbors, have become a predominant computational tool in many domains.

Node Clustering

Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study

1 code implementation24 Aug 2021 Tianlong Chen, Kaixiong Zhou, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang

In view of those, we present the first fair and reproducible benchmark dedicated to assessing the "tricks" of training deep GNNs.

AutoVideo: An Automated Video Action Recognition System

1 code implementation9 Aug 2021 Daochen Zha, Zaid Pervaiz Bhat, Yi-Wei Chen, Yicheng Wang, Sirui Ding, Anmoll Kumar Jain, Mohammad Qazim Bhat, Kwei-Herng Lai, Jiaben Chen, Na Zou, Xia Hu

The pipeline language is quite general so that we can easily enrich AutoVideo with algorithms for various other video-related tasks in the future.

Action Recognition AutoML +1

Dirichlet Energy Constrained Learning for Deep Graph Neural Networks

no code implementations6 Jul 2021 Kaixiong Zhou, Xiao Huang, Daochen Zha, Rui Chen, Li Li, Soo-Hyun Choi, Xia Hu

To this end, we analyze the bottleneck of deep GNNs by leveraging the Dirichlet energy of node embeddings, and propose a generalizable principle to guide the training of deep GNNs.

Generating the Graph Gestalt: Kernel-Regularized Graph Representation Learning

no code implementations29 Jun 2021 Kiarash Zahirnia, Ankita Sakhuja, Oliver Schulte, Parmis Nadaf, Ke Li, Xia Hu

Our experiments demonstrate a significant improvement in the realism of the generated graph structures, typically by 1-2 orders of magnitude of graph structure metrics, compared to leading graph VAEand GAN models.

Graph Representation Learning

Fairness via Representation Neutralization

no code implementations23 Jun 2021 Mengnan Du, Subhabrata Mukherjee, Guanchu Wang, Ruixiang Tang, Ahmed Hassan Awadallah, Xia Hu

This process not only requires a lot of instance-level annotations for sensitive attributes, it also does not guarantee that all fairness sensitive information has been removed from the encoder.

Classification Fairness

Model-Based Counterfactual Synthesizer for Interpretation

no code implementations16 Jun 2021 Fan Yang, Sahan Suresh Alva, Jiahao Chen, Xia Hu

To address these limitations, we propose a Model-based Counterfactual Synthesizer (MCS) framework for interpreting machine learning models.

DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning

2 code implementations11 Jun 2021 Daochen Zha, Jingru Xie, Wenye Ma, Sheng Zhang, Xiangru Lian, Xia Hu, Ji Liu

Games are abstractions of the real world, where artificial agents learn to compete and cooperate with other agents.

Game of Poker Multi-agent Reinforcement Learning

Simplifying Deep Reinforcement Learning via Self-Supervision

1 code implementation10 Jun 2021 Daochen Zha, Kwei-Herng Lai, Kaixiong Zhou, Xia Hu

Supervised regression to demonstrations has been demonstrated to be a stable way to train deep policy networks.

A General Taylor Framework for Unifying and Revisiting Attribution Methods

no code implementations28 May 2021 Huiqi Deng, Na Zou, Mengnan Du, Weifu Chen, Guocan Feng, Xia Hu

However, the attribution problem has not been well-defined, which lacks a unified guideline to the contribution assignment process.

Decision Making

Learning Disentangled Representations for Time Series

no code implementations17 May 2021 Yuening Li, Zhengzhang Chen, Daochen Zha, Mengnan Du, Denghui Zhang, Haifeng Chen, Xia Hu

Motivated by the success of disentangled representation learning in computer vision, we study the possibility of learning semantic-rich time-series representations, which remains unexplored due to three main challenges: 1) sequential data structure introduces complex temporal correlations and makes the latent representations hard to interpret, 2) sequential models suffer from KL vanishing problem, and 3) interpretable semantic concepts for time-series often rely on multiple factors instead of individuals.

Representation Learning Time Series +1

Mutual Information Preserving Back-propagation: Learn to Invert for Faithful Attribution

no code implementations14 Apr 2021 Huiqi Deng, Na Zou, Weifu Chen, Guocan Feng, Mengnan Du, Xia Hu

The basic idea is to learn a source signal by back-propagation such that the mutual information between input and output should be as much as possible preserved in the mutual information between input and the source signal.

Decision Making

DivAug: Plug-in Automated Data Augmentation with Explicit Diversity Maximization

1 code implementation26 Mar 2021 Zirui Liu, Haifeng Jin, Ting-Hsiang Wang, Kaixiong Zhou, Xia Hu

We validate in experiments that the relative gain from automated data augmentation in test accuracy is highly correlated to Variance Diversity.

Data Augmentation

Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU Models

no code implementations NAACL 2021 Mengnan Du, Varun Manjunatha, Rajiv Jain, Ruchi Deshpande, Franck Dernoncourt, Jiuxiang Gu, Tong Sun, Xia Hu

These two observations are further employed to formulate a measurement which can quantify the shortcut degree of each training sample.

Sparse-Interest Network for Sequential Recommendation

no code implementations18 Feb 2021 Qiaoyu Tan, Jianwei Zhang, Jiangchao Yao, Ninghao Liu, Jingren Zhou, Hongxia Yang, Xia Hu

Our sparse-interest module can adaptively infer a sparse set of concepts for each user from the large concept pool and output multiple embeddings accordingly.

Dynamic Memory based Attention Network for Sequential Recommendation

no code implementations18 Feb 2021 Qiaoyu Tan, Jianwei Zhang, Ninghao Liu, Xiao Huang, Hongxia Yang, Jingren Zhou, Xia Hu

It segments the overall long behavior sequence into a series of sub-sequences, then trains the model and maintains a set of memory blocks to preserve long-term interests of users.

Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments

3 code implementations ICLR 2021 Daochen Zha, Wenye Ma, Lei Yuan, Xia Hu, Ji Liu

Unfortunately, methods based on intrinsic rewards often fall short in procedurally-generated environments, where a different environment is generated in each episode so that the agent is not likely to visit the same state more than once.

Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbation

no code implementations18 Jan 2021 Fan Yang, Ninghao Liu, Mengnan Du, Xia Hu

With the wide use of deep neural networks (DNN), model interpretability has become a critical concern, since explainable decisions are preferred in high-stake scenarios.

Efficient Differentiable Neural Architecture Search with Model Parallelism

no code implementations1 Jan 2021 Yi-Wei Chen, Qingquan Song, Xia Hu

Differentiable NAS with supernets that encompass all potential architectures in a large graph cuts down search overhead to few GPU days or less.

Neural Architecture Search

Detecting Interactions from Neural Networks via Topological Analysis

no code implementations NeurIPS 2020 Zirui Liu, Qingquan Song, Kaixiong Zhou, Ting-Hsiang Wang, Ying Shan, Xia Hu

Motivated by the observation, in this paper, we propose to investigate the interaction detection problem from a novel topological perspective by analyzing the connectivity in neural networks.

Deep Serial Number: Computational Watermarking for DNN Intellectual Property Protection

no code implementations17 Nov 2020 Ruixiang Tang, Mengnan Du, Xia Hu

In this paper, we introduce DSN (Deep Serial Number), a new watermarking approach that can prevent the stolen model from being deployed by unauthorized parties.

Knowledge Distillation

Graph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection

no code implementations29 Oct 2020 Imtiaz Ahmed, Travis Galoppo, Xia Hu, Yu Ding

In order to make dimensionality reduction effective for high-dimensional data embedding nonlinear low-dimensional manifold, it is understood that some sort of geodesic distance metric should be used to discriminate the data samples.

Dimensionality Reduction Unsupervised Anomaly Detection

Towards Interaction Detection Using Topological Analysis on Neural Networks

no code implementations25 Oct 2020 Zirui Liu, Qingquan Song, Kaixiong Zhou, Ting Hsiang Wang, Ying Shan, Xia Hu

Detecting statistical interactions between input features is a crucial and challenging task.

Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning

1 code implementation16 Sep 2020 Daochen Zha, Kwei-Herng Lai, Mingyang Wan, Xia Hu

Specifically, existing strategies have been focused on making the top instances more likely to be anomalous based on the feedback.

Anomaly Detection Re-Ranking

Are Interpretations Fairly Evaluated? A Definition Driven Pipeline for Post-Hoc Interpretability

no code implementations16 Sep 2020 Ninghao Liu, Yunsong Meng, Xia Hu, Tie Wang, Bo Long

Recent years have witnessed an increasing number of interpretation methods being developed for improving transparency of NLP models.

Explainable Recommender Systems via Resolving Learning Representations

no code implementations21 Aug 2020 Ninghao Liu, Yong Ge, Li Li, Xia Hu, Rui Chen, Soo-Hyun Choi

Different from previous work, in our model, factor discovery and representation learning are simultaneously conducted, and we are able to handle extra attribute information and knowledge.

Recommendation Systems Representation Learning

A Unified Taylor Framework for Revisiting Attribution Methods

no code implementations21 Aug 2020 Huiqi Deng, Na Zou, Mengnan Du, Weifu Chen, Guocan Feng, Xia Hu

Attribution methods have been developed to understand the decision-making process of machine learning models, especially deep neural networks, by assigning importance scores to individual features.

Decision Making

Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction

no code implementations29 Jun 2020 Qingquan Song, Dehua Cheng, Hanning Zhou, Jiyan Yang, Yuandong Tian, Xia Hu

Click-Through Rate (CTR) prediction is one of the most important machine learning tasks in recommender systems, driving personalized experience for billions of consumers.

Click-Through Rate Prediction Learning-To-Rank +2

AutoRec: An Automated Recommender System

no code implementations26 Jun 2020 Ting-Hsiang Wang, Qingquan Song, Xiaotian Han, Zirui Liu, Haifeng Jin, Xia Hu

To address the need, we present AutoRec, an open-source automated machine learning (AutoML) platform extended from the TensorFlow ecosystem and, to our knowledge, the first framework to leverage AutoML for model search and hyperparameter tuning in deep recommendation models.

AutoML Click-Through Rate Prediction +1

Policy-GNN: Aggregation Optimization for Graph Neural Networks

2 code implementations26 Jun 2020 Kwei-Herng Lai, Daochen Zha, Kaixiong Zhou, Xia Hu

It is a challenging task to develop an effective aggregation strategy for each node, given complex graphs and sparse features.

Node Classification

AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning

no code implementations19 Jun 2020 Yuening Li, Zhengzhang Chen, Daochen Zha, Kaixiong Zhou, Haifeng Jin, Haifeng Chen, Xia Hu

Outlier detection is an important data mining task with numerous practical applications such as intrusion detection, credit card fraud detection, and video surveillance.

Fraud Detection Image Classification +6

Measuring Model Complexity of Neural Networks with Curve Activation Functions

no code implementations16 Jun 2020 Xia Hu, Weiqing Liu, Jiang Bian, Jian Pei

Our results demonstrate that the occurrence of overfitting is positively correlated with the increase of model complexity during training.

An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks

1 code implementation15 Jun 2020 Ruixiang Tang, Mengnan Du, Ninghao Liu, Fan Yang, Xia Hu

In this paper, we investigate a specific security problem called trojan attack, which aims to attack deployed DNN systems relying on the hidden trigger patterns inserted by malicious hackers.

Mitigating Gender Bias in Captioning Systems

1 code implementation15 Jun 2020 Ruixiang Tang, Mengnan Du, Yuening Li, Zirui Liu, Na Zou, Xia Hu

Image captioning has made substantial progress with huge supporting image collections sourced from the web.

Gender Prediction Image Captioning

Towards Deeper Graph Neural Networks with Differentiable Group Normalization

1 code implementation NeurIPS 2020 Kaixiong Zhou, Xiao Huang, Yuening Li, Daochen Zha, Rui Chen, Xia Hu

Graph neural networks (GNNs), which learn the representation of a node by aggregating its neighbors, have become an effective computational tool in downstream applications.

Dual Policy Distillation

1 code implementation7 Jun 2020 Kwei-Herng Lai, Daochen Zha, Yuening Li, Xia Hu

In this work, we introduce dual policy distillation(DPD), a student-student framework in which two learners operate on the same environment to explore different perspectives of the environment and extract knowledge from each other to enhance their learning.

Continuous Control

XGNN: Towards Model-Level Explanations of Graph Neural Networks

no code implementations3 Jun 2020 Hao Yuan, Jiliang Tang, Xia Hu, Shuiwang Ji

Furthermore, our experimental results indicate that the generated graphs can provide guidance on how to improve the trained GNNs.

Graph Generation

iCapsNets: Towards Interpretable Capsule Networks for Text Classification

no code implementations16 May 2020 Zhengyang Wang, Xia Hu, Shuiwang Ji

On the other hand, iCapsNets explore a novel way to explain the model's general behavior, achieving global interpretability.

Classification General Classification +1

Adversarial Attacks and Defenses: An Interpretation Perspective

no code implementations23 Apr 2020 Ninghao Liu, Mengnan Du, Ruocheng Guo, Huan Liu, Xia Hu

In this paper, we review recent work on adversarial attacks and defenses, particularly from the perspective of machine learning interpretation.

Adversarial Attack Adversarial Defense +1

Learning to Hash with Graph Neural Networks for Recommender Systems

no code implementations4 Mar 2020 Qiaoyu Tan, Ninghao Liu, Xing Zhao, Hongxia Yang, Jingren Zhou, Xia Hu

In this work, we investigate the problem of hashing with graph neural networks (GNNs) for high quality retrieval, and propose a simple yet effective discrete representation learning framework to jointly learn continuous and discrete codes.

Graph Representation Learning Recommendation Systems

Multi-Channel Graph Convolutional Networks

no code implementations17 Dec 2019 Kaixiong Zhou, Qingquan Song, Xiao Huang, Daochen Zha, Na Zou, Xia Hu

To further improve the graph representation learning ability, hierarchical GNN has been explored.

Graph Classification Graph Representation Learning

XDeep: An Interpretation Tool for Deep Neural Networks

1 code implementation4 Nov 2019 Fan Yang, Zijian Zhang, Haofan Wang, Yuening Li, Xia Hu

XDeep is an open-source Python package developed to interpret deep models for both practitioners and researchers.

RLCard: A Toolkit for Reinforcement Learning in Card Games

7 code implementations10 Oct 2019 Daochen Zha, Kwei-Herng Lai, Yuanpu Cao, Songyi Huang, Ruzhe Wei, Junyu Guo, Xia Hu

The goal of RLCard is to bridge reinforcement learning and imperfect information games, and push forward the research of reinforcement learning in domains with multiple agents, large state and action space, and sparse reward.

Board Games Game of Poker +1

PyODDS: An End-to-End Outlier Detection System

1 code implementation7 Oct 2019 Yuening Li, Daochen Zha, Na Zou, Xia Hu

PyODDS is an end-to end Python system for outlier detection with database support.

Outlier Detection

Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks

7 code implementations3 Oct 2019 Haofan Wang, Zifan Wang, Mengnan Du, Fan Yang, Zijian Zhang, Sirui Ding, Piotr Mardziel, Xia Hu

Recently, increasing attention has been drawn to the internal mechanisms of convolutional neural networks, and the reason why the network makes specific decisions.

Adversarial Attack Decision Making +1

Contextual Local Explanation for Black Box Classifiers

no code implementations2 Oct 2019 Zijian Zhang, Fan Yang, Haofan Wang, Xia Hu

We introduce a new model-agnostic explanation technique which explains the prediction of any classifier called CLE.

General Classification Image Classification

Sub-Architecture Ensemble Pruning in Neural Architecture Search

no code implementations1 Oct 2019 Yijun Bian, Qingquan Song, Mengnan Du, Jun Yao, Huanhuan Chen, Xia Hu

Neural architecture search (NAS) is gaining more and more attention in recent years due to its flexibility and remarkable capability to reduce the burden of neural network design.

Ensemble Learning Ensemble Pruning +1

Towards Generalizable Deepfake Detection with Locality-aware AutoEncoder

no code implementations13 Sep 2019 Mengnan Du, Shiva Pentyala, Yuening Li, Xia Hu

The analysis further shows that LAE outperforms the state-of-the-arts by 6. 52%, 12. 03%, and 3. 08% respectively on three deepfake detection tasks in terms of generalization accuracy on previously unseen manipulations.

Active Learning DeepFake Detection +2

Fairness in Deep Learning: A Computational Perspective

no code implementations23 Aug 2019 Mengnan Du, Fan Yang, Na Zou, Xia Hu

Deep learning is increasingly being used in high-stake decision making applications that affect individual lives.

Decision Making Fairness

Learning Credible Deep Neural Networks with Rationale Regularization

no code implementations13 Aug 2019 Mengnan Du, Ninghao Liu, Fan Yang, Xia Hu

Recent explainability related studies have shown that state-of-the-art DNNs do not always adopt correct evidences to make decisions.

Text Classification

Deep Structured Cross-Modal Anomaly Detection

no code implementations11 Aug 2019 Yuening Li, Ninghao Liu, Jundong Li, Mengnan Du, Xia Hu

To this end, we propose a novel deep structured anomaly detection framework to identify the cross-modal anomalies embedded in the data.

Anomaly Detection

Techniques for Automated Machine Learning

no code implementations21 Jul 2019 Yi-Wei Chen, Qingquan Song, Xia Hu

Automated machine learning (AutoML) aims to find optimal machine learning solutions automatically given a machine learning problem.

Automated Feature Engineering Feature Engineering

Evaluating Explanation Without Ground Truth in Interpretable Machine Learning

no code implementations16 Jul 2019 Fan Yang, Mengnan Du, Xia Hu

Interpretable Machine Learning (IML) has become increasingly important in many real-world applications, such as autonomous cars and medical diagnosis, where explanations are significantly preferred to help people better understand how machine learning systems work and further enhance their trust towards systems.

Interpretable Machine Learning Medical Diagnosis

XFake: Explainable Fake News Detector with Visualizations

no code implementations8 Jul 2019 Fan Yang, Shiva K. Pentyala, Sina Mohseni, Mengnan Du, Hao Yuan, Rhema Linder, Eric D. Ragan, Shuiwang Ji, Xia Hu

In this demo paper, we present the XFake system, an explainable fake news detector that assists end-users to identify news credibility.

Experience Replay Optimization

no code implementations19 Jun 2019 Daochen Zha, Kwei-Herng Lai, Kaixiong Zhou, Xia Hu

Experience replay enables reinforcement learning agents to memorize and reuse past experiences, just as humans replay memories for the situation at hand.

Continuous Control

Exact and Consistent Interpretation of Piecewise Linear Models Hidden behind APIs: A Closed Form Solution

1 code implementation17 Jun 2019 Zicun Cong, Lingyang Chu, Lanjun Wang, Xia Hu, Jian Pei

More and more AI services are provided through APIs on cloud where predictive models are hidden behind APIs.

Coupled Variational Recurrent Collaborative Filtering

1 code implementation11 Jun 2019 Qingquan Song, Shiyu Chang, Xia Hu

To bridge the gap, in this paper, we propose a Coupled Variational Recurrent Collaborative Filtering (CVRCF) framework based on the idea of Deep Bayesian Learning to handle the streaming recommendation problem.

Recommendation Systems Variational Inference

Deep Bayesian Optimization on Attributed Graphs

3 code implementations31 May 2019 Jiaxu Cui, Bo Yang, Xia Hu

Attributed graphs, which contain rich contextual features beyond just network structure, are ubiquitous and have been observed to benefit various network analytics applications.

Gaussian Processes

Is a Single Vector Enough? Exploring Node Polysemy for Network Embedding

1 code implementation25 May 2019 Ninghao Liu, Qiaoyu Tan, Yuening Li, Hongxia Yang, Jingren Zhou, Xia Hu

Network embedding models are powerful tools in mapping nodes in a network into continuous vector-space representations in order to facilitate subsequent tasks such as classification and link prediction.

General Classification Language Modelling +3

Deep Representation Learning for Social Network Analysis

no code implementations18 Apr 2019 Qiaoyu Tan, Ninghao Liu, Xia Hu

First, we introduce the basic models for learning node representations in homogeneous networks.

Anomaly Detection Link Prediction +1

On Attribution of Recurrent Neural Network Predictions via Additive Decomposition

no code implementations27 Mar 2019 Mengnan Du, Ninghao Liu, Fan Yang, Shuiwang Ji, Xia Hu

REAT decomposes the final prediction of a RNN into additive contribution of each word in the input text.

Decision Making

Multi-Label Adversarial Perturbations

no code implementations2 Jan 2019 Qingquan Song, Haifeng Jin, Xiao Huang, Xia Hu

Experiments on real-world multi-label image classification and ranking problems demonstrate the effectiveness of our proposed frameworks and provide insights of the vulnerability of multi-label deep learning models under diverse targeted attacking strategies.

General Classification Multi-class Classification +3

Techniques for Interpretable Machine Learning

no code implementations31 Jul 2018 Mengnan Du, Ninghao Liu, Xia Hu

Interpretable machine learning tackles the important problem that humans cannot understand the behaviors of complex machine learning models and how these models arrive at a particular decision.

Interpretable Machine Learning

Auto-Keras: An Efficient Neural Architecture Search System

14 code implementations27 Jun 2018 Haifeng Jin, Qingquan Song, Xia Hu

In this paper, we propose a novel framework enabling Bayesian optimization to guide the network morphism for efficient neural architecture search.

Neural Architecture Search

r-Instance Learning for Missing People Tweets Identification

no code implementations28 May 2018 Yang Yang, Haoyan Liu, Xia Hu, Jiawei Zhang, Xiao-Ming Zhang, Zhoujun Li, Philip S. Yu

The number of missing people (i. e., people who get lost) greatly increases in recent years.

Towards Explanation of DNN-based Prediction with Guided Feature Inversion

no code implementations19 Mar 2018 Mengnan Du, Ninghao Liu, Qingquan Song, Xia Hu

While deep neural networks (DNN) have become an effective computational tool, the prediction results are often criticized by the lack of interpretability, which is essential in many real-world applications such as health informatics.

Decision Making

Exact and Consistent Interpretation for Piecewise Linear Neural Networks: A Closed Form Solution

no code implementations17 Feb 2018 Lingyang Chu, Xia Hu, Juhua Hu, Lanjun Wang, Jian Pei

Strong intelligent machines powered by deep neural networks are increasingly deployed as black boxes to make decisions in risk-sensitive domains, such as finance and medical.

Tensor Completion Algorithms in Big Data Analytics

no code implementations28 Nov 2017 Qingquan Song, Hancheng Ge, James Caverlee, Xia Hu

Tensor completion is a problem of filling the missing or unobserved entries of partially observed tensors.

Contextual Outlier Interpretation

no code implementations28 Nov 2017 Ninghao Liu, Donghwa Shin, Xia Hu

Outlier detection plays an essential role in many data-driven applications to identify isolated instances that are different from the majority.

Feature Selection Outlier Interpretation

Deep Style Match for Complementary Recommendation

no code implementations26 Aug 2017 Kui Zhao, Xia Hu, Jiajun Bu, Can Wang

In order to answer these kinds of questions, we attempt to model human sense of style compatibility in this paper.

Common Sense Reasoning Feature Engineering

Neural Collaborative Filtering

35 code implementations WWW 2017 Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, Tat-Seng Chua

When it comes to model the key factor in collaborative filtering -- the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items.

Recommendation Systems Speech Recognition

Attributed Network Embedding for Learning in a Dynamic Environment

no code implementations6 Jun 2017 Jundong Li, Harsh Dani, Xia Hu, Jiliang Tang, Yi Chang, Huan Liu

To our best knowledge, we are the first to tackle this problem with the following two challenges: (1) the inherently correlated network and node attributes could be noisy and incomplete, it necessitates a robust consensus representation to capture their individual properties and correlations; (2) the embedding learning needs to be performed in an online fashion to adapt to the changes accordingly.

Link Prediction Network Embedding +1

Stacked Approximated Regression Machine: A Simple Deep Learning Approach

no code implementations14 Aug 2016 Zhangyang Wang, Shiyu Chang, Qing Ling, Shuai Huang, Xia Hu, Honghui Shi, Thomas S. Huang

With the agreement of my coauthors, I Zhangyang Wang would like to withdraw the manuscript "Stacked Approximated Regression Machine: A Simple Deep Learning Approach".

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