Search Results for author: Liang Zhao

Found 97 papers, 22 papers with code

Heterogeneous Temporal Graph Neural Network

1 code implementation26 Oct 2021 Yujie Fan, Mingxuan Ju, Chuxu Zhang, Liang Zhao, Yanfang Ye

To retain the heterogeneity, intra-relation aggregation is first performed over each slice of HTG to attentively aggregate information of neighbors with the same type of relation, and then intra-relation aggregation is exploited to gather information over different types of relations; to handle temporal dependencies, across-time aggregation is conducted to exchange information across different graph slices over the HTG.

A Method for Inferring Polymers Based on Linear Regression and Integer Programming

no code implementations24 Aug 2021 Ryota Ido, Shengjuan Cao, Jianshen Zhu, Naveed Ahmed Azam, Kazuya Haraguchi, Liang Zhao, Hiroshi Nagamochi, Tatsuya Akutsu

For this, we introduce a new way of representing a polymer as a form of monomer and define new descriptors that feature the structure of polymers.

Molecular Design Based on Artificial Neural Networks, Integer Programming and Grid Neighbor Search

no code implementations23 Aug 2021 Naveed Ahmed Azam, Jianshen Zhu, Kazuya Haraguchi, Liang Zhao, Hiroshi Nagamochi, Tatsuya Akutsu

In the framework, a chemical graph with a target chemical value is inferred as a feasible solution of a mixed integer linear program that represents a prediction function and other requirements on the structure of graphs.

Communication Efficiency in Federated Learning: Achievements and Challenges

no code implementations23 Jul 2021 Osama Shahid, Seyedamin Pouriyeh, Reza M. Parizi, Quan Z. Sheng, Gautam Srivastava, Liang Zhao

Over the years, this has become an emerging technology especially with various data protection and privacy policies being imposed FL allows performing machine learning tasks whilst adhering to these challenges.

Federated Learning

An Inverse QSAR Method Based on Linear Regression and Integer Programming

1 code implementation6 Jul 2021 Jianshen Zhu, Naveed Ahmed Azam, Kazuya Haraguchi, Liang Zhao, Hiroshi Nagamochi, Tatsuya Akutsu

In the framework, we first define a feature vector $f(C)$ of a chemical graph $C$ and construct an ANN that maps $x=f(C)$ to a predicted value $\eta(x)$ of a chemical property $\pi$ to $C$.

RefBERT: Compressing BERT by Referencing to Pre-computed Representations

no code implementations11 Jun 2021 Xinyi Wang, Haiqin Yang, Liang Zhao, Yang Mo, Jianping Shen

Differently, in this paper, we propose RefBERT to leverage the knowledge learned from the teacher, i. e., facilitating the pre-computed BERT representation on the reference sample and compressing BERT into a smaller student model.

Knowledge Distillation

Theoretical Implementation of Stochastic Epileptic Oscillator Using a Tripartite Synaptic Neuronal Network Model

no code implementations26 May 2021 Jiajia Li, Peihua Feng, Liang Zhao, Junying Chen, Mengmeng Du, Yangyang Yu, Ying Wu

Our simulation results showed that the increase of the noise intensity could induce the epileptic seizures state coexisting with the increase of frequency and in vitro epileptic depolarization blocks.

Direct Simultaneous Multi-Image Registration

no code implementations21 May 2021 Zhehua Mao, Liang Zhao, Shoudong Huang, Yiting Fan, Alex Pui-Wai Lee

To obtain the optimal result, we start with formulating a Direct Bundle Adjustment (DBA) problem which jointly optimizes pose parameters of local frames and intensities of panoramic image.

Image Registration

Towards Quantized Model Parallelism for Graph-Augmented MLPs Based on Gradient-Free ADMM framework

no code implementations20 May 2021 Junxiang Wang, Hongyi Li, Zheng Chai, Yongchao Wang, Yue Cheng, Liang Zhao

The Graph Augmented Multi-layer Perceptron (GA-MLP) model is an attractive alternative to Graph Neural Networks (GNNs).


Schematic Memory Persistence and Transience for Efficient and Robust Continual Learning

no code implementations5 May 2021 Yuyang Gao, Giorgio A. Ascoli, Liang Zhao

However, since forgetting is inevitable given bounded memory and unbounded task loads, 'how to reasonably forget' is a problem continual learning must address in order to reduce the performance gap between AIs and humans, in terms of 1) memory efficiency, 2) generalizability, and 3) robustness when dealing with noisy data.

Continual Learning

Cyber Intrusion Detection by Using Deep Neural Networks with Attack-sharing Loss

no code implementations17 Mar 2021 Boxiang Dong, Hui, Wang, Aparna S. Varde, Dawei Li, Bharath K. Samanthula, Weifeng Sun, Liang Zhao

To achieve high detection accuracy on imbalanced data, we design a novel attack-sharing loss function that can effectively move the decision boundary towards the attack classes and eliminates the bias towards the majority/benign class.

Classification General Classification +1

Sign-regularized Multi-task Learning

no code implementations22 Feb 2021 Johnny Torres, Guangji Bai, Junxiang Wang, Liang Zhao, Carmen Vaca, Cristina Abad

Multi-task learning is a framework that enforces different learning tasks to share their knowledge to improve their generalization performance.

Multi-Task Learning

GP: Context-free Grammar Pre-training for Text-to-SQL Parsers

no code implementations25 Jan 2021 Liang Zhao, Hexin Cao, Yunsong Zhao

A new method for Text-to-SQL parsing, Grammar Pre-training (GP), is proposed to decode deep relations between question and database.

SQL Parsing Text-To-Sql

FamDroid: Learning-Based Android Malware Family Classification Using Static Analysis

no code implementations11 Jan 2021 Wenhao fan, Liang Zhao, Jiayang Wang, Ye Chen, Fan Wu, Yuan'an Liu

At present, the main problem of existing research works on Android malware family classification lies in that the extracted features are inadequate to represent the common behavior characteristics of the malware in malicious families, and leveraging a single classifier or a static ensemble classifier is restricted to further improve the accuracy of classification.

Malware Detection Cryptography and Security

A Gradient-based Kernel Approach for Efficient Network Architecture Search

no code implementations1 Jan 2021 Jingjing Xu, Liang Zhao, Junyang Lin, Xu sun, Hongxia Yang

Inspired by our new finding, we explore a simple yet effective network architecture search (NAS) approach that leverages gradient correlation and gradient values to find well-performing architectures.

Image Classification Text Classification

Gradient Descent Resists Compositionality

no code implementations1 Jan 2021 Yuanpeng Li, Liang Zhao, Joel Hestness, Kenneth Church, Mohamed Elhoseiny

In this paper, we argue that gradient descent is one of the reasons that make compositionality learning hard during neural network optimization.

Transferability of Compositionality

no code implementations1 Jan 2021 Yuanpeng Li, Liang Zhao, Joel Hestness, Ka Yee Lun, Kenneth Church, Mohamed Elhoseiny

To our best knowledge, this is the first work to focus on the transferability of compositionality, and it is orthogonal to existing efforts of learning compositional representations in training distribution.

Efficiently Disentangle Causal Representations

no code implementations1 Jan 2021 Yuanpeng Li, Joel Hestness, Mohamed Elhoseiny, Liang Zhao, Kenneth Church

In this paper, we propose a novel approach to efficiently learning disentangled representations with causal mechanisms, based on the difference of conditional probabilities in original and new distributions.

Property Controllable Variational Autoencoder via Invertible Mutual Dependence

no code implementations ICLR 2021 Xiaojie Guo, Yuanqi Du, Liang Zhao

Deep generative models have made important progress towards modeling complex, high dimensional data via learning latent representations.

XLP at SemEval-2020 Task 9: Cross-lingual Models with Focal Loss for Sentiment Analysis of Code-Mixing Language

no code implementations SEMEVAL 2020 Yili Ma, Liang Zhao, Jie Hao

In this paper, we present an approach for sentiment analysis in code-mixed language on twitter defined in SemEval-2020 Task 9.

Sentiment Analysis

pdADMM: parallel deep learning Alternating Direction Method of Multipliers

1 code implementation1 Nov 2020 Junxiang Wang, Zheng Chai, Yue Cheng, Liang Zhao

In this paper, we propose a novel parallel deep learning ADMM framework (pdADMM) to achieve layer parallelism: parameters in each layer of neural networks can be updated independently in parallel.

Online Decision Trees with Fairness

no code implementations15 Oct 2020 Wenbin Zhang, Liang Zhao

In this paper, we propose a novel framework of online decision tree with fairness in the data stream with possible distribution drifting.

Decision Making Fairness

Disentangled Dynamic Graph Deep Generation

1 code implementation14 Oct 2020 Wenbin Zhang, Liming Zhang, Dieter Pfoser, Liang Zhao

Extending existing deep generative models from static to dynamic graphs is a challenging task, which requires to handle the factorization of static and dynamic characteristics as well as mutual interactions among node and edge patterns.

Graph Generation Protein Folding

FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers

no code implementations12 Oct 2020 Zheng Chai, Yujing Chen, Ali Anwar, Liang Zhao, Yue Cheng, Huzefa Rangwala

By bridging the synchronous and asynchronous training through tiering, FedAT minimizes the straggler effect with improved convergence speed and test accuracy.

Federated Learning

A new network-base high-level data classification methodology (Quipus) by modeling attribute-attribute interactions

no code implementations28 Sep 2020 Esteban Wilfredo Vilca Zuñiga, Liang Zhao

The current results show us that this approach improves the accuracy of the high-level classification algorithm based on betweenness centrality.

Classification General Classification

Graph-based Multi-hop Reasoning for Long Text Generation

no code implementations28 Sep 2020 Liang Zhao, Jingjing Xu, Junyang Lin, Yichang Zhang, Hongxia Yang, Xu sun

The reasoning module is responsible for searching skeleton paths from a knowledge graph to imitate the imagination process in the human writing for semantic transfer.

Review Generation Story Generation

A Novel Method for Inference of Acyclic Chemical Compounds with Bounded Branch-height Based on Artificial Neural Networks and Integer Programming

1 code implementation21 Sep 2020 Naveed Ahmed Azam, Jianshen Zhu, Yanming Sun, Yu Shi, Aleksandar Shurbevski, Liang Zhao, Hiroshi Nagamochi, Tatsuya Akutsu

In the second phase, given a target value $y^*$ of property $\pi$, a feature vector $x^*$ is inferred by solving an MILP formulated from the trained ANN so that $\psi(x^*)$ is close to $y^*$ and then a set of chemical structures $G^*$ such that $f(G^*)= x^*$ is enumerated by a graph search algorithm.

Data Structures and Algorithms Computational Engineering, Finance, and Science 05C92, 92E10, 05C30, 68T07, 90C11, 92-04

Factorized Deep Generative Models for Trajectory Generation with Spatiotemporal-Validity Constraints

no code implementations20 Sep 2020 Liming Zhang, Liang Zhao, Dieter Pfoser

Inspired by the success of deep generative neural networks for images and texts, a fast-developing research topic is deep generative models for trajectory data which can learn expressively explanatory models for sophisticated latent patterns.

Variational Inference

A Network-Based High-Level Data Classification Algorithm Using Betweenness Centrality

no code implementations16 Sep 2020 Esteban Vilca, Liang Zhao

Data classification is a major machine learning paradigm, which has been widely applied to solve a large number of real-world problems.

Classification General Classification

Tunable Subnetwork Splitting for Model-parallelism of Neural Network Training

1 code implementation9 Sep 2020 Junxiang Wang, Zheng Chai, Yue Cheng, Liang Zhao

In this paper, we analyze the reason and propose to achieve a compelling trade-off between parallelism and accuracy by a reformulation called Tunable Subnetwork Splitting Method (TSSM), which can tune the decomposition granularity of deep neural networks.

Event Prediction in the Big Data Era: A Systematic Survey

no code implementations19 Jul 2020 Liang Zhao

This paper aims to provide a systematic and comprehensive survey of the technologies, applications, and evaluations of event prediction in the big data era.

Information Retrieval

A Systematic Survey on Deep Generative Models for Graph Generation

no code implementations13 Jul 2020 Xiaojie Guo, Liang Zhao

Graphs are important data representations for describing objects and their relationships, which appear in a wide diversity of real-world scenarios.

Graph Generation

Interpretable Deep Graph Generation with Node-Edge Co-Disentanglement

1 code implementation9 Jun 2020 Xiaojie Guo, Liang Zhao, Zhao Qin, Lingfei Wu, Amarda Shehu, Yanfang Ye

Disentangled representation learning has recently attracted a significant amount of attention, particularly in the field of image representation learning.

Graph Generation Representation Learning

TG-GAN: Continuous-time Temporal Graph Generation with Deep Generative Models

1 code implementation17 May 2020 Liming Zhang, Liang Zhao, Shan Qin, Dieter Pfoser

The recent deep generative models for static graphs that are now being actively developed have achieved significant success in areas such as molecule design.

Graph Generation Protein Folding

Generating Tertiary Protein Structures via an Interpretative Variational Autoencoder

no code implementations8 Apr 2020 Xiaojie Guo, Yuanqi Du, Sivani Tadepalli, Liang Zhao, Amarda Shehu

Much scientific enquiry across disciplines is founded upon a mechanistic treatment of dynamic systems that ties form to function.

Protein Structure Prediction Stochastic Optimization

Dynamic Reconstruction of Deformable Soft-tissue with Stereo Scope in Minimal Invasive Surgery

no code implementations22 Mar 2020 Jingwei Song, Jun Wang, Liang Zhao, Shoudong Huang, Gamini Dissanayake

Our SLAM system can: (1) Incrementally build a live model by progressively fusing new observations with vivid accurate texture.

Simultaneous Localization and Mapping

Deep Multi-attributed Graph Translation with Node-Edge Co-evolution

1 code implementation22 Mar 2020 Xiaojie Guo, Liang Zhao, Cameron Nowzari, Setareh Rafatirad, Houman Homayoun, Sai Manoj Pudukotai Dinakarrao

Then, a spectral graph regularization based on our non-parametric graph Laplacian is proposed in order to learn and maintain the consistency of the predicted nodes and edges.


Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks

no code implementations27 Feb 2020 Zhiqian Chen, Fanglan Chen, Lei Zhang, Taoran Ji, Kaiqun Fu, Liang Zhao, Feng Chen, Lingfei Wu, Charu Aggarwal, Chang-Tien Lu

Deep learning's success has been widely recognized in a variety of machine learning tasks, including image classification, audio recognition, and natural language processing.

Image Classification Natural Language Understanding +1

Chaotic Phase Synchronization and Desynchronization in an Oscillator Network for Object Selection

no code implementations13 Feb 2020 Fabricio A Breve, Marcos G. Quiles, Liang Zhao, Elbert E. N. Macau

Oscillators in the network representing the salient object in a given scene are phase synchronized, while no phase synchronization occurs for background objects.

Particle Competition and Cooperation for Semi-Supervised Learning with Label Noise

no code implementations12 Feb 2020 Fabricio Aparecido Breve, Liang Zhao, Marcos Gonçalves Quiles

Computer simulations show the classification accuracy of the proposed method when applied to some artificial and real-world data sets, in which we introduce increasing amounts of label noise.

Classification General Classification

Code-Bridged Classifier (CBC): A Low or Negative Overhead Defense for Making a CNN Classifier Robust Against Adversarial Attacks

no code implementations16 Jan 2020 Farnaz Behnia, Ali Mirzaeian, Mohammad Sabokrou, Sai Manoj, Tinoosh Mohsenin, Khaled N. Khasawneh, Liang Zhao, Houman Homayoun, Avesta Sasan

In this paper, we propose Code-Bridged Classifier (CBC), a framework for making a Convolutional Neural Network (CNNs) robust against adversarial attacks without increasing or even by decreasing the overall models' computational complexity.

Denoising Image Classification

Automated Analysis of Femoral Artery Calcification Using Machine Learning Techniques

no code implementations12 Dec 2019 Liang Zhao, Brendan Odigwe, Susan Lessner, Daniel G. Clair, Firas Mussa, Homayoun Valafar

We report an object tracking algorithm that combines geometrical constraints, thresholding, and motion detection for tracking of the descending aorta and the network of major arteries that branch from the aorta including the iliac and femoral arteries.

Motion Detection Object Tracking

Learning to Recommend via Meta Parameter Partition

no code implementations4 Dec 2019 Liang Zhao, Yang Wang, daxiang dong, Hao Tian

The fixed part, capturing user invariant features, is shared by all users and is learned during offline meta learning stage.


Efficient Global String Kernel with Random Features: Beyond Counting Substructures

no code implementations25 Nov 2019 Lingfei Wu, Ian En-Hsu Yen, Siyu Huo, Liang Zhao, Kun Xu, Liang Ma, Shouling Ji, Charu Aggarwal

In this paper, we present a new class of global string kernels that aims to (i) discover global properties hidden in the strings through global alignments, (ii) maintain positive-definiteness of the kernel, without introducing a diagonal dominant kernel matrix, and (iii) have a training cost linear with respect to not only the length of the string but also the number of training string samples.

TITAN: A Spatiotemporal Feature Learning Framework for Traffic Incident Duration Prediction

no code implementations20 Nov 2019 Kaiqun Fu, Taoran Ji, Liang Zhao, Chang-Tien Lu

In this paper, we propose a traffic incident duration prediction model that simultaneously predicts the impact of the traffic incidents and identifies the critical groups of temporal features via a multi-task learning framework.

Multi-Task Learning

Compositional Generalization for Primitive Substitutions

1 code implementation IJCNLP 2019 Yuanpeng Li, Liang Zhao, Jian-Yu Wang, Joel Hestness

Compositional generalization is a basic mechanism in human language learning, but current neural networks lack such ability.

Few-Shot Learning Machine Translation +1

Multi-stage Deep Classifier Cascades for Open World Recognition

1 code implementation26 Aug 2019 Xiaojie Guo, Amir Alipour-Fanid, Lingfei Wu, Hemant Purohit, Xiang Chen, Kai Zeng, Liang Zhao

At present, object recognition studies are mostly conducted in a closed lab setting with classes in test phase typically in training phase.

Object Recognition

DynGraph2Seq: Dynamic-Graph-to-Sequence Interpretable Learning for Health Stage Prediction in Online Health Forums

no code implementations22 Aug 2019 Yuyang Gao, Lingfei Wu, Houman Homayoun, Liang Zhao

In this paper, we first formulate the transition of user activities as a dynamic graph with multi-attributed nodes, then formalize the health stage inference task as a dynamic graph-to-sequence learning problem, and hence propose a novel dynamic graph-to-sequence neural networks architecture (DynGraph2Seq) to address all the challenges.


CBOWRA: A Representation Learning Approach for Medication Anomaly Detection

no code implementations20 Aug 2019 Liang Zhao, Zhiyuan Ma, Yangming Zhou, Kai Wang, Shengping Liu, Ju Gao

Electronic health record is an important source for clinical researches and applications, and errors inevitably occur in the data, which could lead to severe damages to both patients and hospital services.

Anomaly Detection Representation Learning

Pyramid: Machine Learning Framework to Estimate the Optimal Timing and Resource Usage of a High-Level Synthesis Design

no code implementations29 Jul 2019 Hosein Mohammadi Makrani, Farnoud Farahmand, Hossein Sayadi, Sara Bondi, Sai Manoj Pudukotai Dinakarrao, Liang Zhao, Avesta Sasan, Houman Homayoun, Setareh Rafatirad

HLS tools offer a plethora of techniques to optimize designs for both area and performance, but resource usage and timing reports of HLS tools mostly deviate from the post-implementation results.

Efficient two step optimization for large embedded deformation graph based SLAM

no code implementations20 Jun 2019 Jingwei Song, Fang Bai, Liang Zhao, Shoudong Huang, Rong Xiong

In this paper, we propose an approach to decouple nodes of deformation graph in large scale dense deformable SLAM and keep the estimation time to be constant.

CUR Low Rank Approximation of a Matrix at Sublinear Cost

no code implementations10 Jun 2019 Victor Y. Pan, Qi Luan, John Svadlenka, Liang Zhao

Low rank approximation of a matrix (hereafter LRA) is a highly important area of Numerical Linear and Multilinear Algebra and Data Mining and Analysis.

Numerical Analysis Numerical Analysis

ADMM for Efficient Deep Learning with Global Convergence

1 code implementation31 May 2019 Junxiang Wang, Fuxun Yu, Xiang Chen, Liang Zhao

However, as an emerging domain, several challenges remain, including 1) The lack of global convergence guarantees, 2) Slow convergence towards solutions, and 3) Cubic time complexity with regard to feature dimensions.

Stochastic Optimization

Interpreting and Evaluating Neural Network Robustness

no code implementations10 May 2019 Fuxun Yu, Zhuwei Qin, Chenchen Liu, Liang Zhao, Yanzhi Wang, Xiang Chen

Recently, adversarial deception becomes one of the most considerable threats to deep neural networks.

Adversarial Attack

iRDA Method for Sparse Convolutional Neural Networks

no code implementations ICLR 2019 Xiaodong Jia, Liang Zhao, Lian Zhang, Juncai He, Jinchao Xu

We propose a new approach, known as the iterative regularized dual averaging (iRDA), to improve the efficiency of convolutional neural networks (CNN) by significantly reducing the redundancy of the model without reducing its accuracy.

Learning Good Representation via Continuous Attention

no code implementations29 Mar 2019 Liang Zhao, Wei Xu

In this paper we present our scientific discovery that good representation can be learned via continuous attention during the interaction between Unsupervised Learning(UL) and Reinforcement Learning(RL) modules driven by intrinsic motivation.

Object Recognition

Global Fire Season Severity Analysis and Forecasting

1 code implementation11 Mar 2019 Leonardo N. Ferreira, Didier A. Vega-Oliveros, Liang Zhao, Manoel F. Cardoso, Elbert E. N. Macau

In this paper, we evaluate the possibility of using historical data from 2003 to 2017 of active fire detections (NASA's MODIS MCD14ML C6) and time series forecasting methods to estimate global fire season severity (FSS), here defined as the accumulated fire detections in a season.


CircConv: A Structured Convolution with Low Complexity

no code implementations28 Feb 2019 Siyu Liao, Zhe Li, Liang Zhao, Qinru Qiu, Yanzhi Wang, Bo Yuan

Deep neural networks (DNNs), especially deep convolutional neural networks (CNNs), have emerged as the powerful technique in various machine learning applications.

Estimating the Circuit Deobfuscating Runtime based on Graph Deep Learning

no code implementations14 Feb 2019 Zhiqian Chen, Gaurav Kolhe, Setareh Rafatirad, Sai Manoj P. D., Houman Homayoun, Liang Zhao, Chang-Tien Lu

Deobfuscation runtime could have a large span ranging from few milliseconds to thousands of years or more, depending on the number and layouts of the ICs and camouflaged gates.

The Effect of Time Series Distance Functions on Functional Climate Networks

2 code implementations8 Feb 2019 Leonardo N. Ferreira, Nicole C. R. Ferreira, Maria Livia L. M. Gava, Liang Zhao, Elbert E. N. Macau

In this context, functional climate networks can be constructed using a spatiotemporal climate dataset and a suitable time series distance function.

Data Analysis, Statistics and Probability Atmospheric and Oceanic Physics

Robust Regression via Online Feature Selection under Adversarial Data Corruption

no code implementations5 Feb 2019 Xuchao Zhang, Shuo Lei, Liang Zhao, Arnold P. Boedihardjo, Chang-Tien Lu

The presence of data corruption in user-generated streaming data, such as social media, motivates a new fundamental problem that learns reliable regression coefficient when features are not accessible entirely at one time.

Feature Selection

WALL-E: An Efficient Reinforcement Learning Research Framework

1 code implementation18 Jan 2019 Tianbing Xu, Andrew Zhang, Liang Zhao

There are two halves to RL systems: experience collection time and policy learning time.

From Node Embedding to Graph Embedding: Scalable Global Graph Kernel via Random Features

no code implementations NIPS 2018 2018 Lingfei Wu, Ian En-Hsu Yen, Kun Xu, Liang Zhao, Yinglong Xia, Michael Witbrock

Graph kernels are one of the most important methods for graph data analysis and have been successfully applied in diverse applications.

Graph Embedding

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

Interpreting Adversarial Robustness: A View from Decision Surface in Input Space

no code implementations ICLR 2019 Fuxun Yu, ChenChen Liu, Yanzhi Wang, Liang Zhao, Xiang Chen

One popular hypothesis of neural network generalization is that the flat local minima of loss surface in parameter space leads to good generalization.

Organ at Risk Segmentation in Head and Neck CT Images by Using a Two-Stage Segmentation Framework Based on 3D U-Net

no code implementations25 Aug 2018 Yueyue Wang, Liang Zhao, Zhijian Song, Manning Wang

Accurate segmentation of organ at risk (OAR) play a critical role in the treatment planning of image guided radiation treatment of head and neck cancer.

Make $\ell_1$ Regularization Effective in Training Sparse CNN

no code implementations11 Jul 2018 Juncai He, Xiaodong Jia, Jinchao Xu, Lian Zhang, Liang Zhao

Compressed Sensing using $\ell_1$ regularization is among the most powerful and popular sparsification technique in many applications, but why has it not been used to obtain sparse deep learning model such as convolutional neural network (CNN)?

Distributed Self-Paced Learning in Alternating Direction Method of Multipliers

no code implementations6 Jul 2018 Xuchao Zhang, Liang Zhao, Zhiqian Chen, Chang-Tien Lu

One key issue in SPL is the training process required for each instance weight depends on the other samples and thus cannot easily be run in a distributed manner in a large-scale dataset.

Learning to Explore via Meta-Policy Gradient

no code implementations ICML 2018 Tianbing Xu, Qiang Liu, Liang Zhao, Jian Peng

The performance of off-policy learning, including deep Q-learning and deep deterministic policy gradient (DDPG), critically depends on the choice of the exploration policy.

Continuous Control Q-Learning

Deep Graph Translation

2 code implementations25 May 2018 Xiaojie Guo, Lingfei Wu, Liang Zhao

To achieve this, we propose a novel Graph-Translation-Generative Adversarial Networks (GT-GAN) which will generate a graph translator from input to target graphs.


Learning to Explore with Meta-Policy Gradient

no code implementations13 Mar 2018 Tianbing Xu, Qiang Liu, Liang Zhao, Jian Peng

The performance of off-policy learning, including deep Q-learning and deep deterministic policy gradient (DDPG), critically depends on the choice of the exploration policy.


Occlusion Aware Unsupervised Learning of Optical Flow

no code implementations CVPR 2018 Yang Wang, Yi Yang, Zhenheng Yang, Liang Zhao, Peng Wang, Wei Xu

Especially on KITTI dataset where abundant unlabeled samples exist, our unsupervised method outperforms its counterpart trained with supervised learning.

Optical Flow Estimation

Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey

1 code implementation12 Nov 2017 Hamed Jelodar, Yongli Wang, Chi Yuan, Xia Feng, Xiahui Jiang, Yanchao Li, Liang Zhao

Topic modeling is one of the most powerful techniques in text mining for data mining, latent data discovery, and finding relationships among data, text documents.

Unsupervised Learning of Geometry with Edge-aware Depth-Normal Consistency

no code implementations10 Nov 2017 Zhenheng Yang, Peng Wang, Wei Xu, Liang Zhao, Ramakant Nevatia

Learning to reconstruct depths in a single image by watching unlabeled videos via deep convolutional network (DCN) is attracting significant attention in recent years.

Depth Estimation

Feature learning in feature-sample networks using multi-objective optimization

no code implementations25 Oct 2017 Filipe Alves Neto Verri, Renato Tinós, Liang Zhao

We show that the enhanced network contains more information and can be exploited to improve the performance of machine learning methods.

Online and Distributed Robust Regressions under Adversarial Data Corruption

no code implementations2 Oct 2017 Xuchao Zhang, Liang Zhao, Arnold P. Boedihardjo, Chang-Tien Lu

In today's era of big data, robust least-squares regression becomes a more challenging problem when considering the adversarial corruption along with explosive growth of datasets.

A Generic Framework for Interesting Subspace Cluster Detection in Multi-attributed Networks

no code implementations15 Sep 2017 Feng Chen, Baojian Zhou, Adil Alim, Liang Zhao

As a case study, we specialize SG-Pursuit to optimize a number of well-known score functions for two typical tasks, including detection of coherent dense and anomalous connected subspace clusters in real-world networks.

Feature Selection

Unsupervised Learning Layers for Video Analysis

no code implementations24 May 2017 Liang Zhao, Yang Wang, Yi Yang, Wei Xu

This paper presents two unsupervised learning layers (UL layers) for label-free video analysis: one for fully connected layers, and the other for convolutional ones.

Object Localization

Nonconvex Generalization of Alternating Direction Method of Multipliers for Nonlinear Equality Constrained Problems

no code implementations9 May 2017 Junxiang Wang, Liang Zhao

The classic Alternating Direction Method of Multipliers (ADMM) is a popular framework to solve linear-equality constrained problems.

Optimization and Control Social and Information Networks

Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank

no code implementations ICML 2017 Liang Zhao, Siyu Liao, Yanzhi Wang, Zhe Li, Jian Tang, Victor Pan, Bo Yuan

Recently low displacement rank (LDR) matrices, or so-called structured matrices, have been proposed to compress large-scale neural networks.

Network Unfolding Map by Edge Dynamics Modeling

no code implementations3 Mar 2016 Filipe Alves Neto Verri, Paulo Roberto Urio, Liang Zhao

Labeled vertices generate new particles that compete against rival particles for edge domination.

Time Series Clustering via Community Detection in Networks

1 code implementation19 Aug 2015 Leonardo N. Ferreira, Liang Zhao

In this paper, we propose a technique for time series clustering using community detection in complex networks.

Community Detection Time Series +2

A feasible roadmap for developing volumetric probability atlas of localized prostate cancer

no code implementations15 Sep 2014 Liang Zhao, Jianhua Xuan, Yue Wang

A statistical volumetric model, showing the probability map of localized prostate cancer within the host anatomical structure, has been developed from 90 optically-imaged surgical specimens.

Spatial Neural Networks and their Functional Samples: Similarities and Differences

no code implementations3 May 2014 Lucas Antiqueira, Liang Zhao

Models of neural networks have proven their utility in the development of learning algorithms in computer science and in the theoretical study of brain dynamics in computational neuroscience.

EEG Time Series

High Level Pattern Classification via Tourist Walks in Networks

no code implementations7 May 2013 Thiago Christiano Silva, Liang Zhao

Out of various high level perspectives that can be utilized to capture semantic meaning, we utilize the dynamical features that are generated from a tourist walker in a networked environment.

Classification General Classification

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