Search Results for author: Junzhou Huang

Found 130 papers, 51 papers with code

Pan-cancer computational histopathology reveals tumor mutational burden status through weakly-supervised deep learning

no code implementations7 Apr 2022 Siteng Chen, Jinxi Xiang, Xiyue Wang, Jun Zhang, Sen yang, Junzhou Huang, Wei Yang, Junhua Zheng, Xiao Han

In comparison with the state-of-the-art TMB prediction model from previous publications, our multiscale model achieved better performance over previously reported models.

whole slide images

Hypergraph Convolutional Networks via Equivalency between Hypergraphs and Undirected Graphs

no code implementations31 Mar 2022 Jiying Zhang, Fuyang Li, Xi Xiao, Tingyang Xu, Yu Rong, Junzhou Huang, Yatao Bian

As a powerful tool for modeling complex relationships, hypergraphs are gaining popularity from the graph learning community.

Graph Learning

Boost Test-Time Performance with Closed-Loop Inference

no code implementations21 Mar 2022 Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Guanghui Xu, Haokun Li, Peilin Zhao, Junzhou Huang, YaoWei Wang, Mingkui Tan

Motivated by this, we propose to predict those hard-classified test samples in a looped manner to boost the model performance.

Auxiliary Learning

Equivariant Graph Mechanics Networks with Constraints

1 code implementation12 Mar 2022 Wenbing Huang, Jiaqi Han, Yu Rong, Tingyang Xu, Fuchun Sun, Junzhou Huang

The core of GMN is that it represents, by generalized coordinates, the forward kinematics information (positions and velocities) of a structural object.

Transformer for Graphs: An Overview from Architecture Perspective

1 code implementation17 Feb 2022 Erxue Min, Runfa Chen, Yatao Bian, Tingyang Xu, Kangfei Zhao, Wenbing Huang, Peilin Zhao, Junzhou Huang, Sophia Ananiadou, Yu Rong

In this survey, we provide a comprehensive review of various Graph Transformer models from the architectural design perspective.

Recent Advances in Reliable Deep Graph Learning: Adversarial Attack, Inherent Noise, and Distribution Shift

no code implementations15 Feb 2022 Bingzhe Wu, Jintang Li, Chengbin Hou, Guoji Fu, Yatao Bian, Liang Chen, Junzhou Huang

Despite the progress, applying DGL to real-world applications faces a series of reliability threats including adversarial attacks, inherent noise, and distribution shift.

Adversarial Attack Graph Learning

Not All Low-Pass Filters are Robust in Graph Convolutional Networks

1 code implementation NeurIPS 2021 Heng Chang, Yu Rong, Tingyang Xu, Yatao Bian, Shiji Zhou, Xin Wang, Junzhou Huang, Wenwu Zhu

Graph Convolutional Networks (GCNs) are promising deep learning approaches in learning representations for graph-structured data.

Functionally Regionalized Knowledge Transfer for Low-resource Drug Discovery

no code implementations NeurIPS 2021 Huaxiu Yao, Ying WEI, Long-Kai Huang, Ding Xue, Junzhou Huang, Zhenhui (Jessie) Li

More recently, there has been a surge of interest in employing machine learning approaches to expedite the drug discovery process where virtual screening for hit discovery and ADMET prediction for lead optimization play essential roles.

Drug Discovery Meta-Learning +1

Graph Convolutional Module for Temporal Action Localization in Videos

no code implementations1 Dec 2021 Runhao Zeng, Wenbing Huang, Mingkui Tan, Yu Rong, Peilin Zhao, Junzhou Huang, Chuang Gan

To this end, we propose a general graph convolutional module (GCM) that can be easily plugged into existing action localization methods, including two-stage and one-stage paradigms.

Action Recognition

Constrained Graph Mechanics Networks

no code implementations ICLR 2022 Wenbing Huang, Jiaqi Han, Yu Rong, Tingyang Xu, Fuchun Sun, Junzhou Huang

In this manner, the geometrical constraints are implicitly and naturally encoded in the forward kinematics.

Weakly Supervised Graph Clustering

no code implementations29 Sep 2021 Tian Bian, Tingyang Xu, Yu Rong, Wenbing Huang, Xi Xiao, Peilin Zhao, Junzhou Huang, Hong Cheng

Graph Clustering, which clusters the nodes of a graph given its collection of node features and edge connections in an unsupervised manner, has long been researched in graph learning and is essential in certain applications.

Graph Clustering Graph Learning

PI-GNN: Towards Robust Semi-Supervised Node Classification against Noisy Labels

no code implementations29 Sep 2021 Xuefeng Du, Tian Bian, Yu Rong, Bo Han, Tongliang Liu, Tingyang Xu, Wenbing Huang, Junzhou Huang

Semi-supervised node classification on graphs is a fundamental problem in graph mining that uses a small set of labeled nodes and many unlabeled nodes for training, so that its performance is quite sensitive to the quality of the node labels.

Graph Mining Node Classification

Local Augmentation for Graph Neural Networks

no code implementations8 Sep 2021 Songtao Liu, Hanze Dong, Lanqing Li, Tingyang Xu, Yu Rong, Peilin Zhao, Junzhou Huang, Dinghao Wu

Data augmentation has been widely used in image data and linguistic data but remains under-explored for Graph Neural Networks (GNNs).

TVT: Transferable Vision Transformer for Unsupervised Domain Adaptation

1 code implementation12 Aug 2021 Jinyu Yang, Jingjing Liu, Ning Xu, Junzhou Huang

With the recent exponential increase in applying Vision Transformer (ViT) to vision tasks, the capability of ViT in adapting cross-domain knowledge, however, remains unexplored in the literature.

Transfer Learning Unsupervised Domain Adaptation

Frustratingly Easy Transferability Estimation

no code implementations17 Jun 2021 Long-Kai Huang, Ying WEI, Yu Rong, Qiang Yang, Junzhou Huang

Transferability estimation has been an essential tool in selecting a pre-trained model and the layers in it for transfer learning, to transfer, so as to maximize the performance on a target task and prevent negative transfer.

Mutual Information Estimation Transfer Learning

PI-GNN: A Novel Perspective on Semi-Supervised Node Classification against Noisy Labels

no code implementations14 Jun 2021 Xuefeng Du, Tian Bian, Yu Rong, Bo Han, Tongliang Liu, Tingyang Xu, Wenbing Huang, Junzhou Huang

Semi-supervised node classification, as a fundamental problem in graph learning, leverages unlabeled nodes along with a small portion of labeled nodes for training.

Graph Learning Node Classification

Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning

no code implementations ICLR 2022 Yatao Bian, Yu Rong, Tingyang Xu, Jiaxiang Wu, Andreas Krause, Junzhou Huang

By running fixed point iteration for multiple steps, we achieve a trajectory of the valuations, among which we define the valuation with the best conceivable decoupling error as the Variational Index.

Variational Inference

Learning Graphon Autoencoders for Generative Graph Modeling

no code implementations29 May 2021 Hongteng Xu, Peilin Zhao, Junzhou Huang, Dixin Luo

A linear graphon factorization model works as a decoder, leveraging the latent representations to reconstruct the induced graphons (and the corresponding observed graphs).

Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge

no code implementations26 May 2021 Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Honglei Zhang, Peng Cui, Xin Wang, Wenwu Zhu, Junzhou Huang

We investigate the theoretical connections between graph signal processing and graph embedding models and formulate the graph embedding model as a general graph signal process with a corresponding graph filter.

Adversarial Attack Graph Embedding +1

Exploring Robustness of Unsupervised Domain Adaptation in Semantic Segmentation

1 code implementation ICCV 2021 Jinyu Yang, Chunyuan Li, Weizhi An, Hehuan Ma, Yuzhi Guo, Yu Rong, Peilin Zhao, Junzhou Huang

Recent studies imply that deep neural networks are vulnerable to adversarial examples -- inputs with a slight but intentional perturbation are incorrectly classified by the network.

Semantic Segmentation Unsupervised Domain Adaptation

Similarity-aware Positive Instance Sampling for Graph Contrastive Pre-training

no code implementations NeurIPS 2021 Xueyi Liu, Yu Rong, Tingyang Xu, Fuchun Sun, Wenbing Huang, Junzhou Huang

To remedy this issue, we propose to select positive graph instances directly from existing graphs in the training set, which ultimately maintains the legality and similarity to the target graphs.

Contrastive Learning Graph Classification +1

EBM-Fold: Fully-Differentiable Protein Folding Powered by Energy-based Models

no code implementations11 May 2021 Jiaxiang Wu, Shitong Luo, Tao Shen, Haidong Lan, Sheng Wang, Junzhou Huang

In this paper, we propose a fully-differentiable approach for protein structure optimization, guided by a data-driven generative network.

Denoising Protein Folding +1

FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks

1 code implementation14 Apr 2021 Chaoyang He, Keshav Balasubramanian, Emir Ceyani, Carl Yang, Han Xie, Lichao Sun, Lifang He, Liangwei Yang, Philip S. Yu, Yu Rong, Peilin Zhao, Junzhou Huang, Murali Annavaram, Salman Avestimehr

FedGraphNN is built on a unified formulation of graph FL and contains a wide range of datasets from different domains, popular GNN models, and FL algorithms, with secure and efficient system support.

Federated Learning Molecular Property Prediction

Deep Implicit Statistical Shape Models for 3D Medical Image Delineation

1 code implementation7 Apr 2021 Ashwin Raju, Shun Miao, Dakai Jin, Le Lu, Junzhou Huang, Adam P. Harrison

DISSMs use a deep implicit surface representation to produce a compact and descriptive shape latent space that permits statistical models of anatomical variance.

Liver Segmentation Pose Estimation

Diversified Multiscale Graph Learning with Graph Self-Correction

no code implementations17 Mar 2021 Yuzhao Chen, Yatao Bian, Jiying Zhang, Xi Xiao, Tingyang Xu, Yu Rong, Junzhou Huang

Though the multiscale graph learning techniques have enabled advanced feature extraction frameworks, the classic ensemble strategy may show inferior performance while encountering the high homogeneity of the learnt representation, which is caused by the nature of existing graph pooling methods.

Ensemble Learning Graph Classification +1

Pareto-Frontier-aware Neural Architecture Generation for Diverse Budgets

no code implementations27 Feb 2021 Yong Guo, Yaofo Chen, Yin Zheng, Qi Chen, Peilin Zhao, Jian Chen, Junzhou Huang, Mingkui Tan

To this end, we propose a Pareto-Frontier-aware Neural Architecture Generator (NAG) which takes an arbitrary budget as input and produces the Pareto optimal architecture for the target budget.

Provably Improved Context-Based Offline Meta-RL with Attention and Contrastive Learning

no code implementations22 Feb 2021 Lanqing Li, Yuanhao Huang, Mingzhe Chen, Siteng Luo, Dijun Luo, Junzhou Huang

Meta-learning for offline reinforcement learning (OMRL) is an understudied problem with tremendous potential impact by enabling RL algorithms in many real-world applications.

Contrastive Learning Meta-Learning +2

Towards Accurate and Compact Architectures via Neural Architecture Transformer

2 code implementations20 Feb 2021 Yong Guo, Yin Zheng, Mingkui Tan, Qi Chen, Zhipeng Li, Jian Chen, Peilin Zhao, Junzhou Huang

To address this issue, we propose a Neural Architecture Transformer++ (NAT++) method which further enlarges the set of candidate transitions to improve the performance of architecture optimization.

Neural Architecture Search

Learning Diverse-Structured Networks for Adversarial Robustness

1 code implementation3 Feb 2021 Xuefeng Du, Jingfeng Zhang, Bo Han, Tongliang Liu, Yu Rong, Gang Niu, Junzhou Huang, Masashi Sugiyama

In adversarial training (AT), the main focus has been the objective and optimizer while the model has been less studied, so that the models being used are still those classic ones in standard training (ST).

Adversarial Robustness

Pareto-Frontier-aware Neural Architecture Search

no code implementations1 Jan 2021 Yong Guo, Yaofo Chen, Yin Zheng, Peilin Zhao, Jian Chen, Junzhou Huang, Mingkui Tan

To find promising architectures under different budgets, existing methods may have to perform an independent search for each budget, which is very inefficient and unnecessary.

Neural Architecture Search

Hierarchical Graph Capsule Network

1 code implementation16 Dec 2020 Jinyu Yang, Peilin Zhao, Yu Rong, Chaochao Yan, Chunyuan Li, Hehuan Ma, Junzhou Huang

Graph Neural Networks (GNNs) draw their strength from explicitly modeling the topological information of structured data.

Graph Classification

Adversarial Sparse Transformer for Time Series Forecasting

1 code implementation NeurIPS 2020 Sifan Wu, Xi Xiao, Qianggang Ding, Peilin Zhao, Ying WEI, Junzhou Huang

Specifically, AST adopts a Sparse Transformer as the generator to learn a sparse attention map for time series forecasting, and uses a discriminator to improve the prediction performance from sequence level.

Multivariate Time Series Forecasting Probabilistic Time Series Forecasting +1

Deep Multimodal Fusion by Channel Exchanging

1 code implementation NeurIPS 2020 Yikai Wang, Wenbing Huang, Fuchun Sun, Tingyang Xu, Yu Rong, Junzhou Huang

Deep multimodal fusion by using multiple sources of data for classification or regression has exhibited a clear advantage over the unimodal counterpart on various applications.

Image-to-Image Translation Semantic Segmentation +1

On Self-Distilling Graph Neural Network

no code implementations4 Nov 2020 Yuzhao Chen, Yatao Bian, Xi Xiao, Yu Rong, Tingyang Xu, Junzhou Huang

Furthermore, the inefficient training process of teacher-student knowledge distillation also impedes its applications in GNN models.

Graph Embedding Knowledge Distillation

RetroXpert: Decompose Retrosynthesis Prediction like a Chemist

1 code implementation NeurIPS 2020 Chaochao Yan, Qianggang Ding, Peilin Zhao, Shuangjia Zheng, Jinyu Yang, Yang Yu, Junzhou Huang

Retrosynthesis is the process of recursively decomposing target molecules into available building blocks.

Graph Information Bottleneck for Subgraph Recognition

1 code implementation ICLR 2021 Junchi Yu, Tingyang Xu, Yu Rong, Yatao Bian, Junzhou Huang, Ran He

In this paper, we propose a framework of Graph Information Bottleneck (GIB) for the subgraph recognition problem in deep graph learning.

Denoising Graph Classification +1

Dirichlet Graph Variational Autoencoder

1 code implementation NeurIPS 2020 Jia Li, Tomasyu Yu, Jiajin Li, Honglei Zhang, Kangfei Zhao, Yu Rong, Hong Cheng, Junzhou Huang

In this work, we present Dirichlet Graph Variational Autoencoder (DGVAE) with graph cluster memberships as latent factors.

Graph Clustering Graph Generation

Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networks

1 code implementation23 Sep 2020 Jiawen Yao, Xinliang Zhu, Jitendra Jonnagaddala, Nicholas Hawkins, Junzhou Huang

We evaluated our methods on two large cancer whole slide images datasets and our results suggest that the proposed approach is more effective and suitable for large datasets and has better interpretability in locating important patterns and features that contribute to accurate cancer survival predictions.

Deep Attention Multiple Instance Learning +2

Microscope Based HER2 Scoring System

no code implementations15 Sep 2020 Jun Zhang, Kuan Tian, Pei Dong, Haocheng Shen, Kezhou Yan, Jianhua Yao, Junzhou Huang, Xiao Han

Recently, artificial intelligence (AI) has been used in various disease diagnosis to improve diagnostic accuracy and reliability, but the interpretation of diagnosis results is still an open problem.

Tackling Over-Smoothing for General Graph Convolutional Networks

no code implementations22 Aug 2020 Wenbing Huang, Yu Rong, Tingyang Xu, Fuchun Sun, Junzhou Huang

Increasing the depth of GCN, which is expected to permit more expressivity, is shown to incur performance detriment especially on node classification.

Node Classification

Improving Generative Adversarial Networks with Local Coordinate Coding

1 code implementation28 Jul 2020 Jiezhang Cao, Yong Guo, Qingyao Wu, Chunhua Shen, Junzhou Huang, Mingkui Tan

In this paper, rather than sampling from the predefined prior distribution, we propose an LCCGAN model with local coordinate coding (LCC) to improve the performance of generating data.

Improving Generalization in Meta-learning via Task Augmentation

1 code implementation26 Jul 2020 Huaxiu Yao, Long-Kai Huang, Linjun Zhang, Ying WEI, Li Tian, James Zou, Junzhou Huang, Zhenhui Li

Moreover, both MetaMix and Channel Shuffle outperform state-of-the-art results by a large margin across many datasets and are compatible with existing meta-learning algorithms.

Meta-Learning

Inverse Graph Identification: Can We Identify Node Labels Given Graph Labels?

no code implementations12 Jul 2020 Tian Bian, Xi Xiao, Tingyang Xu, Yu Rong, Wenbing Huang, Peilin Zhao, Junzhou Huang

Upon a formal discussion of the variants of IGI, we choose a particular case study of node clustering by making use of the graph labels and node features, with an assistance of a hierarchical graph that further characterizes the connections between different graphs.

Community Detection Graph Attention +2

Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search

1 code implementation ICML 2020 Yong Guo, Yaofo Chen, Yin Zheng, Peilin Zhao, Jian Chen, Junzhou Huang, Mingkui Tan

With the proposed search strategy, our Curriculum Neural Architecture Search (CNAS) method significantly improves the search efficiency and finds better architectures than existing NAS methods.

Neural Architecture Search

Self-Supervised Graph Transformer on Large-Scale Molecular Data

1 code implementation NeurIPS 2020 Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying WEI, Wenbing Huang, Junzhou Huang

We pre-train GROVER with 100 million parameters on 10 million unlabelled molecules -- the biggest GNN and the largest training dataset in molecular representation learning.

Molecular Property Prediction Representation Learning

Co-Heterogeneous and Adaptive Segmentation from Multi-Source and Multi-Phase CT Imaging Data: A Study on Pathological Liver and Lesion Segmentation

no code implementations ECCV 2020 Ashwin Raju, Chi-Tung Cheng, Yunakai Huo, Jinzheng Cai, Junzhou Huang, Jing Xiao, Le Lu, ChienHuang Liao, Adam P. Harrison

In medical imaging, organ/pathology segmentation models trained on current publicly available and fully-annotated datasets usually do not well-represent the heterogeneous modalities, phases, pathologies, and clinical scenarios encountered in real environments.

Computed Tomography (CT) Domain Adaptation +1

Multi-View Graph Neural Networks for Molecular Property Prediction

no code implementations17 May 2020 Hehuan Ma, Yatao Bian, Yu Rong, Wenbing Huang, Tingyang Xu, Weiyang Xie, Geyan Ye, Junzhou Huang

Guided by this observation, we present Multi-View Graph Neural Network (MV-GNN), a multi-view message passing architecture to enable more accurate predictions of molecular properties.

Drug Discovery Molecular Property Prediction

Towards Fast Adaptation of Neural Architectures with Meta Learning

1 code implementation ICLR 2020 Dongze Lian, Yin Zheng, Yintao Xu, Yanxiong Lu, Leyu Lin, Peilin Zhao, Junzhou Huang, Shenghua Gao

Recently, Neural Architecture Search (NAS) has been successfully applied to multiple artificial intelligence areas and shows better performance compared with hand-designed networks.

Few-Shot Learning Neural Architecture Search

COVID-DA: Deep Domain Adaptation from Typical Pneumonia to COVID-19

1 code implementation30 Apr 2020 Yifan Zhang, Shuaicheng Niu, Zhen Qiu, Ying WEI, Peilin Zhao, Jianhua Yao, Junzhou Huang, Qingyao Wu, Mingkui Tan

There are two main challenges: 1) the discrepancy of data distributions between domains; 2) the task difference between the diagnosis of typical pneumonia and COVID-19.

COVID-19 Diagnosis Domain Adaptation

Real-Time Semantic Segmentation via Auto Depth, Downsampling Joint Decision and Feature Aggregation

no code implementations31 Mar 2020 Peng Sun, Jiaxiang Wu, Songyuan Li, Peiwen Lin, Junzhou Huang, Xi Li

To satisfy the stringent requirements on computational resources in the field of real-time semantic segmentation, most approaches focus on the hand-crafted design of light-weight segmentation networks.

Neural Architecture Search Real-Time Semantic Segmentation

Disturbance-immune Weight Sharing for Neural Architecture Search

no code implementations29 Mar 2020 Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Yong Guo, Peilin Zhao, Junzhou Huang, Mingkui Tan

To alleviate the performance disturbance issue, we propose a new disturbance-immune update strategy for model updating.

Neural Architecture Search

Spectral Graph Attention Network with Fast Eigen-approximation

no code implementations16 Mar 2020 Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Somayeh Sojoudi, Junzhou Huang, Wenwu Zhu

In this paper, we first introduce the attention mechanism in the spectral domain of graphs and present Spectral Graph Attention Network (SpGAT) that learns representations for different frequency components regarding weighted filters and graph wavelets bases.

Graph Attention Node Classification +1

Context-Aware Domain Adaptation in Semantic Segmentation

no code implementations9 Mar 2020 Jinyu Yang, Weizhi An, Chaochao Yan, Peilin Zhao, Junzhou Huang

To achieve this goal, we design two cross-domain attention modules to adapt context dependencies from both spatial and channel views.

Semantic Segmentation Unsupervised Domain Adaptation

Cost-Sensitive Portfolio Selection via Deep Reinforcement Learning

no code implementations6 Mar 2020 Yifan Zhang, Peilin Zhao, Qingyao Wu, Bin Li, Junzhou Huang, Mingkui Tan

This task, however, has two main difficulties: (i) the non-stationary price series and complex asset correlations make the learning of feature representation very hard; (ii) the practicality principle in financial markets requires controlling both transaction and risk costs.

reinforcement-learning

Graph Representation Learning via Graphical Mutual Information Maximization

1 code implementation4 Feb 2020 Zhen Peng, Wenbing Huang, Minnan Luo, Qinghua Zheng, Yu Rong, Tingyang Xu, Junzhou Huang

The richness in the content of various information networks such as social networks and communication networks provides the unprecedented potential for learning high-quality expressive representations without external supervision.

Graph Representation Learning Link Prediction +2

Adversarial Domain Adaptation for Cell Segmentation

no code implementations MIDL 2019 Mohammad Minhazul Haq, Junzhou Huang

In this paper, we propose a network named CellSegUDA for cell segmentation on the unlabeled dataset (target domain).

Cell Segmentation Unsupervised Domain Adaptation

Adversarial Attack on Community Detection by Hiding Individuals

1 code implementation22 Jan 2020 Jia Li, Honglei Zhang, Zhichao Han, Yu Rong, Hong Cheng, Junzhou Huang

It has been demonstrated that adversarial graphs, i. e., graphs with imperceptible perturbations added, can cause deep graph models to fail on node/graph classification tasks.

Adversarial Attack Community Detection +1

Graph Ordering: Towards the Optimal by Learning

no code implementations18 Jan 2020 Kangfei Zhao, Yu Rong, Jeffrey Xu Yu, Junzhou Huang, Hao Zhang

However, regardless of the fruitful progress, for some kind of graph applications, such as graph compression and edge partition, it is very hard to reduce them to some graph representation learning tasks.

Combinatorial Optimization Community Detection +3

Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks

1 code implementation17 Jan 2020 Tian Bian, Xi Xiao, Tingyang Xu, Peilin Zhao, Wenbing Huang, Yu Rong, Junzhou Huang

Meanwhile, detecting rumors from such massive information in social media is becoming an arduous challenge.

Discrimination-aware Network Pruning for Deep Model Compression

1 code implementation4 Jan 2020 Jing Liu, Bohan Zhuang, Zhuangwei Zhuang, Yong Guo, Junzhou Huang, Jinhui Zhu, Mingkui Tan

In this paper, we propose a simple-yet-effective method called discrimination-aware channel pruning (DCP) to choose the channels that actually contribute to the discriminative power.

Face Recognition Image Classification +2

Online Adaptive Asymmetric Active Learning with Limited Budgets

1 code implementation18 Nov 2019 Yifan Zhang, Peilin Zhao, Shuaicheng Niu, Qingyao Wu, JieZhang Cao, Junzhou Huang, Mingkui Tan

In these problems, there are two key challenges: the query budget is often limited; the ratio between classes is highly imbalanced.

Active Learning Anomaly Detection

Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis

1 code implementation17 Nov 2019 Yifan Zhang, Ying WEI, Peilin Zhao, Shuaicheng Niu, Qingyao Wu, Mingkui Tan, Junzhou Huang

In this paper, we seek to exploit rich labeled data from relevant domains to help the learning in the target task with unsupervised domain adaptation (UDA).

Unsupervised Domain Adaptation

NAT: Neural Architecture Transformer for Accurate and Compact Architectures

1 code implementation NeurIPS 2019 Yong Guo, Yin Zheng, Mingkui Tan, Qi Chen, Jian Chen, Peilin Zhao, Junzhou Huang

To verify the effectiveness of the proposed strategies, we apply NAT on both hand-crafted architectures and NAS based architectures.

Neural Architecture Search

Re-balancing Variational Autoencoder Loss for Molecule Sequence Generation

1 code implementation1 Oct 2019 Chaochao Yan, Sheng Wang, Jinyu Yang, Tingyang Xu, Junzhou Huang

We investigate the posterior collapse problem of current RNN-based VAEs for molecule sequence generation.

Octave Graph Convolutional Network

no code implementations25 Sep 2019 Heng Chang, Yu Rong, Somayeh Sojoudi, Junzhou Huang, Wenwu Zhu

Many variants of Graph Convolutional Networks (GCNs) for representation learning have been proposed recently and have achieved fruitful results in various domains.

Node Classification Representation Learning

Graph Convolutional Networks for Temporal Action Localization

1 code implementation ICCV 2019 Runhao Zeng, Wenbing Huang, Mingkui Tan, Yu Rong, Peilin Zhao, Junzhou Huang, Chuang Gan

Then we apply the GCNs over the graph to model the relations among different proposals and learn powerful representations for the action classification and localization.

Action Classification Temporal Action Localization

Transferable Neural Processes for Hyperparameter Optimization

no code implementations7 Sep 2019 Ying Wei, Peilin Zhao, Huaxiu Yao, Junzhou Huang

Automated machine learning aims to automate the whole process of machine learning, including model configuration.

Hyperparameter Optimization Transfer Learning

A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models

1 code implementation4 Aug 2019 Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Honglei Zhang, Peng Cui, Wenwu Zhu, Junzhou Huang

To this end, we begin by investigating the theoretical connections between graph signal processing and graph embedding models in a principled way and formulate the graph embedding model as a general graph signal process with corresponding graph filter.

Adversarial Attack Graph Embedding +1

DropEdge: Towards Deep Graph Convolutional Networks on Node Classification

4 code implementations ICLR 2020 Yu Rong, Wenbing Huang, Tingyang Xu, Junzhou Huang

\emph{Over-fitting} and \emph{over-smoothing} are two main obstacles of developing deep Graph Convolutional Networks (GCNs) for node classification.

Classification General Classification +1

Unsupervised Adversarial Graph Alignment with Graph Embedding

no code implementations1 Jul 2019 Chaoqi Chen, Weiping Xie, Tingyang Xu, Yu Rong, Wenbing Huang, Xinghao Ding, Yue Huang, Junzhou Huang

In this paper, we propose an Unsupervised Adversarial Graph Alignment (UAGA) framework to learn a cross-graph alignment between two embedding spaces of different graphs in a fully unsupervised fashion (\emph{i. e.,} no existing anchor links and no users' personal profile or attribute information is available).

Graph Embedding Link Prediction

Cascade-BGNN: Toward Efficient Self-supervised Representation Learning on Large-scale Bipartite Graphs

1 code implementation27 Jun 2019 Chaoyang He, Tian Xie, Yu Rong, Wenbing Huang, Junzhou Huang, Xiang Ren, Cyrus Shahabi

Existing techniques either cannot be scaled to large-scale bipartite graphs that have limited labels or cannot exploit the unique structure of bipartite graphs, which have distinct node features in two domains.

Recommendation Systems Representation Learning

Semi-supervised Learning with Contrastive Predicative Coding

no code implementations25 May 2019 Jiaxing Wang, Yin Zheng, Xiaoshuang Chen, Junzhou Huang, Jian Cheng

Semi-supervised learning (SSL) provides a powerful framework for leveraging unlabeled data when labels are limited or expensive to obtain.

RaFM: Rank-Aware Factorization Machines

1 code implementation18 May 2019 Xiaoshuang Chen, Yin Zheng, Jiaxing Wang, Wenye Ma, Junzhou Huang

Factorization machines (FM) are a popular model class to learn pairwise interactions by a low-rank approximation.

General Classification

Semi-Supervised Graph Classification: A Hierarchical Graph Perspective

1 code implementation10 Apr 2019 Jia Li, Yu Rong, Hong Cheng, Helen Meng, Wenbing Huang, Junzhou Huang

We study the node classification problem in the hierarchical graph where a `node' is a graph instance, e. g., a user group in the above example.

Classification General Classification +4

Weakly Supervised Dense Event Captioning in Videos

no code implementations NeurIPS 2018 Xuguang Duan, Wenbing Huang, Chuang Gan, Jingdong Wang, Wenwu Zhu, Junzhou Huang

Dense event captioning aims to detect and describe all events of interest contained in a video.

PocketFlow: An Automated Framework for Compressing and Accelerating Deep Neural Networks

1 code implementation NIPS Workshop CDNNRIA 2018 Jiaxiang Wu, Yao Zhang, Haoli Bai, Huasong Zhong, Jinlong Hou, Wei Liu, Wenbing Huang, Junzhou Huang

Deep neural networks are widely used in various domains, but the prohibitive computational complexity prevents their deployment on mobile devices.

Model Compression

Hyperparameter Learning via Distributional Transfer

1 code implementation NeurIPS 2019 Ho Chung Leon Law, Peilin Zhao, Lucian Chan, Junzhou Huang, Dino Sejdinovic

Bayesian optimisation is a popular technique for hyperparameter learning but typically requires initial exploration even in cases where similar prior tasks have been solved.

Bayesian Optimisation

Learning Joint Wasserstein Auto-Encoders for Joint Distribution Matching

no code implementations27 Sep 2018 JieZhang Cao, Yong Guo, Langyuan Mo, Peilin Zhao, Junzhou Huang, Mingkui Tan

We study the joint distribution matching problem which aims at learning bidirectional mappings to match the joint distribution of two domains.

Frame Unsupervised Image-To-Image Translation +2

Dual Reconstruction Nets for Image Super-Resolution with Gradient Sensitive Loss

no code implementations19 Sep 2018 Yong Guo, Qi Chen, Jian Chen, Junzhou Huang, Yanwu Xu, JieZhang Cao, Peilin Zhao, Mingkui Tan

However, most deep learning methods employ feed-forward architectures, and thus the dependencies between LR and HR images are not fully exploited, leading to limited learning performance.

Image Super-Resolution

Weakly Supervised Region Proposal Network and Object Detection

no code implementations ECCV 2018 Peng Tang, Xinggang Wang, Angtian Wang, Yongluan Yan, Wenyu Liu, Junzhou Huang, Alan Yuille

The Convolutional Neural Network (CNN) based region proposal generation method (i. e. region proposal network), trained using bounding box annotations, is an essential component in modern fully supervised object detectors.

Region Proposal Weakly Supervised Object Detection

Controllable Image-to-Video Translation: A Case Study on Facial Expression Generation

no code implementations9 Aug 2018 Lijie Fan, Wenbing Huang, Chuang Gan, Junzhou Huang, Boqing Gong

The recent advances in deep learning have made it possible to generate photo-realistic images by using neural networks and even to extrapolate video frames from an input video clip.

Image-to-Image Translation Translation +1

On the Acceleration of L-BFGS with Second-Order Information and Stochastic Batches

no code implementations14 Jul 2018 Jie Liu, Yu Rong, Martin Takac, Junzhou Huang

This paper proposes a framework of L-BFGS based on the (approximate) second-order information with stochastic batches, as a novel approach to the finite-sum minimization problems.

Transfer Learning via Learning to Transfer

no code implementations ICML 2018 Ying WEI, Yu Zhang, Junzhou Huang, Qiang Yang

In transfer learning, what and how to transfer are two primary issues to be addressed, as different transfer learning algorithms applied between a source and a target domain result in different knowledge transferred and thereby the performance improvement in the target domain.

Transfer Learning

Nonparametric Topic Modeling with Neural Inference

no code implementations18 Jun 2018 Xuefei Ning, Yin Zheng, Zhuxi Jiang, Yu Wang, Huazhong Yang, Junzhou Huang

Moreover, we also propose HiTM-VAE, where the document-specific topic distributions are generated in a hierarchical manner.

Topic Models

Adversarial Learning with Local Coordinate Coding

no code implementations ICML 2018 Jiezhang Cao, Yong Guo, Qingyao Wu, Chunhua Shen, Junzhou Huang, Mingkui Tan

Generative adversarial networks (GANs) aim to generate realistic data from some prior distribution (e. g., Gaussian noises).

Adaptive Cost-sensitive Online Classification

no code implementations6 Apr 2018 Peilin Zhao, Yifan Zhang, Min Wu, Steven C. H. Hoi, Mingkui Tan, Junzhou Huang

Cost-Sensitive Online Classification has drawn extensive attention in recent years, where the main approach is to directly online optimize two well-known cost-sensitive metrics: (i) weighted sum of sensitivity and specificity; (ii) weighted misclassification cost.

Anomaly Detection Classification +1

End-to-End Learning of Motion Representation for Video Understanding

1 code implementation CVPR 2018 Lijie Fan, Wenbing Huang, Chuang Gan, Stefano Ermon, Boqing Gong, Junzhou Huang

Despite the recent success of end-to-end learned representations, hand-crafted optical flow features are still widely used in video analysis tasks.

Action Recognition Optical Flow Estimation +1

Robust Actor-Critic Contextual Bandit for Mobile Health (mHealth) Interventions

no code implementations27 Feb 2018 Feiyun Zhu, Jun Guo, Ruoyu Li, Junzhou Huang

Extensive experiment results on two datasets demonstrate that our method can achieve almost identical results compared with state-of-the-art contextual bandit methods on the dataset without outliers, and significantly outperform those state-of-the-art methods on the badly noised dataset with outliers in a variety of parameter settings.

Decision Making

Adaptive Graph Convolutional Neural Networks

2 code implementations10 Jan 2018 Ruoyu Li, Sheng Wang, Feiyun Zhu, Junzhou Huang

Graph Convolutional Neural Networks (Graph CNNs) are generalizations of classical CNNs to handle graph data such as molecular data, point could and social networks.

Metric Learning

A Bayesian Nonparametric Topic Model with Variational Auto-Encoders

no code implementations ICLR 2018 Xuefei Ning, Yin Zheng, Zhuxi Jiang, Yu Wang, Huazhong Yang, Junzhou Huang

On the other hand, different with the other BNP topic models, the inference of iTM-VAE is modeled by neural networks, which has rich representation capacity and can be computed in a simple feed-forward manner.

Representation Learning Topic Models

Robust Contextual Bandit via the Capped-$\ell_{2}$ norm

no code implementations17 Aug 2017 Feiyun Zhu, Xinliang Zhu, Sheng Wang, Jiawen Yao, Junzhou Huang

In the critic updating, the capped-$\ell_{2}$ norm is used to measure the approximation error, which prevents outliers from dominating our objective.

Decision Making

Group-driven Reinforcement Learning for Personalized mHealth Intervention

1 code implementation14 Aug 2017 Feiyun Zhu, Jun Guo, Zheng Xu, Peng Liao, Junzhou Huang

Due to the popularity of smartphones and wearable devices nowadays, mobile health (mHealth) technologies are promising to bring positive and wide impacts on people's health.

Decision Making reinforcement-learning

Learning Graph While Training: An Evolving Graph Convolutional Neural Network

no code implementations10 Aug 2017 Ruoyu Li, Junzhou Huang

Convolution Neural Networks on Graphs are important generalization and extension of classical CNNs.

WSISA: Making Survival Prediction From Whole Slide Histopathological Images

no code implementations CVPR 2017 Xinliang Zhu, Jiawen Yao, Feiyun Zhu, Junzhou Huang

Different from existing state-of-the-arts image-based survival models which extract features using some patches from small regions of WSIs, the proposed framework can efficiently exploit and utilize all discriminative patterns in WSIs to predict patients' survival status.

Survival Analysis Survival Prediction

Cohesion-based Online Actor-Critic Reinforcement Learning for mHealth Intervention

no code implementations25 Mar 2017 Feiyun Zhu, Peng Liao, Xinliang Zhu, Yaowen Yao, Junzhou Huang

In this paper, we propose a network cohesion constrained (actor-critic) Reinforcement Learning (RL) method for mHealth.

Decision Making online learning +1

Track Facial Points in Unconstrained Videos

no code implementations9 Sep 2016 Xi Peng, Qiong Hu, Junzhou Huang, Dimitris N. Metaxas

Our approach takes advantage of part-based representation and cascade regression for robust and efficient alignment on each frame.

Frame Incremental Learning

Scalable Sequential Spectral Clustering

1 code implementation AAAI 2016 Yeqing Li, Junzhou Huang, Wei Liu

In the past decades, Spectral Clustering (SC) has become one of the most effective clustering approaches.

graph construction Image Clustering

SIRF: Simultaneous Image Registration and Fusion in A Unified Framework

no code implementations18 Nov 2014 Chen Chen, Yeqing Li, Wei Liu, Junzhou Huang

In this paper, we propose a novel method for image fusion with a high-resolution panchromatic image and a low-resolution multispectral image at the same geographical location.

Image Registration

Fast Iteratively Reweighted Least Squares Algorithms for Analysis-Based Sparsity Reconstruction

no code implementations18 Nov 2014 Chen Chen, Junzhou Huang, Lei He, Hongsheng Li

The convergence rate of the proposed algorithm is almost the same as that of the traditional IRLS algorithms, that is, exponentially fast.

Compressive Sensing

Image Fusion with Local Spectral Consistency and Dynamic Gradient Sparsity

no code implementations CVPR 2014 Chen Chen, Yeqing Li, Wei Liu, Junzhou Huang

In this paper, we propose a novel method for image fusion from a high resolution panchromatic image and a low resolution multispectral image at the same geographical location.

Compressive Sensing MRI with Wavelet Tree Sparsity

no code implementations NeurIPS 2012 Chen Chen, Junzhou Huang

On the other side, some algorithms have been proposed for tree sparsity regularization, but few of them has validated the benefit of tree structure in CS-MRI.

Compressive Sensing

Forest Sparsity for Multi-channel Compressive Sensing

no code implementations20 Nov 2012 Chen Chen, Yeqing Li, Junzhou Huang

In this paper, we investigate a new compressive sensing model for multi-channel sparse data where each channel can be represented as a hierarchical tree and different channels are highly correlated.

Compressive Sensing

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