Search Results for author: Liang Zhao

Found 184 papers, 63 papers with code

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 +1

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 Electroencephalogram (EEG) +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.

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.

Clustering Community Detection +2

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.

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.

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

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

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

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.

regression

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.

BIG-bench Machine Learning

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

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.

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

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.

Q-Learning Reinforcement Learning (RL)

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.

Management Translation

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 +2

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.

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)?

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.

Segmentation

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.

Adversarial Robustness

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

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

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 regression

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

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.

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.

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.

Applications

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 Object Recognition +2

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.

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

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

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 math.NA (Numerical Analysis)

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.

Vocal Bursts Valence Prediction

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.

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 BIG-bench Machine Learning +1

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.

Graph-to-Sequence

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

DEEP GRAPH SPECTRAL EVOLUTION NETWORKS FOR GRAPH TOPOLOGICAL TRANSFORMATION

no code implementations25 Sep 2019 Liang Zhao, Qingzhe Li, Negar Etemadyrad, Xiaojie Guo

On the other hand, graph topological evolution has been investigated in the graph signal processing domain historically, but it involves intensive labors to manually determine suitable prescribed spectral models and prohibitive difficulty to fit their potential combinations and compositions.

Graph Learning

Gradient-free Neural Network Training by Multi-convex Alternating Optimization

no code implementations25 Sep 2019 Junxiang Wang, Fuxun Yu, Xiang Chen, Liang Zhao

To overcome these drawbacks, alternating minimization-based methods for deep neural network optimization have attracted fast-increasing attention recently.

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 +2

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.

Management Multi-Task Learning

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.

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.

Meta-Learning

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.

BIG-bench Machine Learning Motion Detection +1

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

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

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.

Object

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

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.

Translation

Generating Tertiary Protein Structures via an Interpretative Variational Autoencoder

1 code implementation8 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

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.

Attribute Generative Adversarial Network +2

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.

Disentanglement Graph Generation

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

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

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.

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 +1

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 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

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.

Attribute Classification +1

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 Sentence +1

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

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

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

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.

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.

Disentanglement

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.

Out-of-Distribution Generalization

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 +1

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

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.

Sentence SQL Parsing +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

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.

General Classification Intrusion Detection

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

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

1 code implementation20 May 2021 Junxiang Wang, Hongyi Li, Zheng Chai, Yongchao Wang, Yue Cheng, Liang Zhao

Theoretical convergence to a (quantized) stationary point of the pdADMM-G algorithm and the pdADMM-G-Q algorithm is provided with a sublinear convergence rate $o(1/k)$, where $k$ is the number of iterations.

Quantization

DSR: Direct Simultaneous Registration for Multiple 3D Images

1 code implementation21 May 2021 Zhehua Mao, Liang Zhao, Shoudong Huang, Yiting Fan, Alex Pui-Wai Lee

This paper presents a novel algorithm named Direct Simultaneous Registration (DSR) that registers a collection of 3D images in a simultaneous fashion without specifying any reference image, feature extraction and matching, or information loss or reuse.

Image Registration Pose Estimation

Transition behavior of the seizure dynamics modulated by the astrocyte inositol triphosphate noise

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

Our simulation results show that the increase of the IP3 noise intensity induces the depolarization-block epileptic seizures together with an increase in neuronal firing frequency.

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

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 $C$.

regression

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.

BIG-bench Machine Learning Federated Learning

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.

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.

regression

GraphGT: Machine Learning Datasets for Graph Generation and Transformation

1 code implementation NeurIPS Workshop AI4Scien 2021 Yuanqi Du, Shiyu Wang, Xiaojie Guo, Hengning Cao, Shujie Hu, Junji Jiang, Aishwarya Varala, Abhinav Angirekula, Liang Zhao

Graph generation, which learns from known graphs and discovers novel graphs, has great potential in numerous research topics like drug design and mobility synthesis and is one of the fastest-growing domains recently due to its promise for discovering new knowledge.

BIG-bench Machine Learning Graph Generation +1

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.

Relation Representation Learning

KNAS: Green Neural Architecture Search

1 code implementation26 Nov 2021 Jingjing Xu, Liang Zhao, Junyang Lin, Rundong Gao, Xu sun, Hongxia Yang

Many existing neural architecture search (NAS) solutions rely on downstream training for architecture evaluation, which takes enormous computations.

Image Classification Neural Architecture Search +2

Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-sentence Dependency Graph

1 code implementation1 Dec 2021 Liyan Xu, Xuchao Zhang, Bo Zong, Yanchi Liu, Wei Cheng, Jingchao Ni, Haifeng Chen, Liang Zhao, Jinho D. Choi

We target the task of cross-lingual Machine Reading Comprehension (MRC) in the direct zero-shot setting, by incorporating syntactic features from Universal Dependencies (UD), and the key features we use are the syntactic relations within each sentence.

Machine Reading Comprehension Sentence

Representation Learning on Spatial Networks

1 code implementation NeurIPS 2021 Zheng Zhang, Liang Zhao

Specifically, a provably information-lossless and roto-translation invariant representation of spatial information on networks is presented.

Representation Learning Translation

Adaptive Kernel Graph Neural Network

1 code implementation8 Dec 2021 Mingxuan Ju, Shifu Hou, Yujie Fan, Jianan Zhao, Liang Zhao, Yanfang Ye

To solve this problem, in this paper, we propose a novel framework - i. e., namely Adaptive Kernel Graph Neural Network (AKGNN) - which learns to adapt to the optimal graph kernel in a unified manner at the first attempt.

Representation Learning

A Convergent ADMM Framework for Efficient Neural Network Training

1 code implementation22 Dec 2021 Junxiang Wang, Hongyi Li, Liang Zhao

As a well-known optimization framework, the Alternating Direction Method of Multipliers (ADMM) has achieved tremendous success in many classification and regression applications.

Efficient Neural Network

Do Multi-Lingual Pre-trained Language Models Reveal Consistent Token Attributions in Different Languages?

no code implementations23 Dec 2021 Junxiang Wang, Xuchao Zhang, Bo Zong, Yanchi Liu, Wei Cheng, Jingchao Ni, Haifeng Chen, Liang Zhao

During the past several years, a surge of multi-lingual Pre-trained Language Models (PLMs) has been proposed to achieve state-of-the-art performance in many cross-lingual downstream tasks.

Efficiently Disentangle Causal Representations

1 code implementation6 Jan 2022 Yuanpeng Li, Joel Hestness, Mohamed Elhoseiny, Liang Zhao, Kenneth Church

This paper proposes an efficient approach to learning disentangled representations with causal mechanisms based on the difference of conditional probabilities in original and new distributions.

Deep Generative Model for Periodic Graphs

1 code implementation28 Jan 2022 Shiyu Wang, Xiaojie Guo, Liang Zhao

To address them, this paper proposes Periodical-Graph Disentangled Variational Auto-encoder (PGD-VAE), a new deep generative models for periodic graphs that can automatically learn, disentangle, and generate local and global graph patterns.

Aligning Eyes between Humans and Deep Neural Network through Interactive Attention Alignment

1 code implementation6 Feb 2022 Yuyang Gao, Tong Sun, Liang Zhao, Sungsoo Hong

We propose a novel framework of Interactive Attention Alignment (IAA) that aims at realizing human-steerable Deep Neural Networks (DNNs).

Gender Classification

Black-box Node Injection Attack for Graph Neural Networks

no code implementations18 Feb 2022 Mingxuan Ju, Yujie Fan, Yanfang Ye, Liang Zhao

Graph Neural Networks (GNNs) have drawn significant attentions over the years and been broadly applied to vital fields that require high security standard such as product recommendation and traffic forecasting.

Product Recommendation

Interpretable Molecular Graph Generation via Monotonic Constraints

no code implementations28 Feb 2022 Yuanqi Du, Xiaojie Guo, Amarda Shehu, Liang Zhao

Recent advances in deep graph generative models treat molecule design as graph generation problems which provide new opportunities toward the breakthrough of this long-lasting problem.

Disentanglement Drug Discovery +2

Disentangled Spatiotemporal Graph Generative Models

no code implementations28 Feb 2022 Yuanqi Du, Xiaojie Guo, Hengning Cao, Yanfang Ye, Liang Zhao

Spatiotemporal graph represents a crucial data structure where the nodes and edges are embedded in a geometric space and can evolve dynamically over time.

Disentanglement Graph Generation +1

Task-specific Inconsistency Alignment for Domain Adaptive Object Detection

1 code implementation CVPR 2022 Liang Zhao, LiMin Wang

To address this issue, in this paper, we propose Task-specific Inconsistency Alignment (TIA), by developing a new alignment mechanism in separate task spaces, improving the performance of the detector on both subtasks.

Object object-detection +1

Logit Normalization for Long-tail Object Detection

no code implementations31 Mar 2022 Liang Zhao, Yao Teng, LiMin Wang

Real-world data exhibiting skewed distributions pose a serious challenge to existing object detectors.

Object object-detection +1

Stability of China's Stock Market: Measure and Forecast by Ricci Curvature on Network

no code implementations14 Apr 2022 Xinyu Wang, Liang Zhao, Ning Zhang, Liu Feng, Haibo Lin

As far as we know, this is the first paper to apply Ricci curvature to forecast the systemic stability of domestic stock market, and our results show that Ricci curvature has good explanatory power for the market stability and can be a good indicator to judge the future risk and volatility of the domestic market.

Time Series Time Series Analysis

Temporal Domain Generalization with Drift-Aware Dynamic Neural Networks

1 code implementation21 May 2022 Guangji Bai, Chen Ling, Liang Zhao

Temporal domain generalization is a promising yet extremely challenging area where the goal is to learn models under temporally changing data distributions and generalize to unseen data distributions following the trends of the change.

Domain Generalization Graph Generation

Distributed Graph Neural Network Training with Periodic Stale Representation Synchronization

no code implementations31 May 2022 Zheng Chai, Guangji Bai, Liang Zhao, Yue Cheng

Traditional sampling-based methods accelerate GNN training by dropping edges and nodes, which impairs the graph integrity and model performance.

Graph Embedding Knowledge Graphs +1

An Invertible Graph Diffusion Neural Network for Source Localization

1 code implementation18 Jun 2022 Junxiang Wang, Junji Jiang, Liang Zhao

This paper aims to establish a generic framework of invertible graph diffusion models for source localization on graphs, namely Invertible Validity-aware Graph Diffusion (IVGD), to handle major challenges including 1) Difficulty to leverage knowledge in graph diffusion models for modeling their inverse processes in an end-to-end fashion, 2) Difficulty to ensure the validity of the inferred sources, and 3) Efficiency and scalability in source inference.

Misinformation

Source Localization of Graph Diffusion via Variational Autoencoders for Graph Inverse Problems

1 code implementation24 Jun 2022 Chen Ling, Junji Jiang, Junxiang Wang, Liang Zhao

Different from most traditional source localization methods, this paper focuses on a probabilistic manner to account for the uncertainty of different candidate sources.

RES: A Robust Framework for Guiding Visual Explanation

1 code implementation27 Jun 2022 Yuyang Gao, Tong Steven Sun, Guangji Bai, Siyi Gu, Sungsoo Ray Hong, Liang Zhao

Despite the fast progress of explanation techniques in modern Deep Neural Networks (DNNs) where the main focus is handling "how to generate the explanations", advanced research questions that examine the quality of the explanation itself (e. g., "whether the explanations are accurate") and improve the explanation quality (e. g., "how to adjust the model to generate more accurate explanations when explanations are inaccurate") are still relatively under-explored.

Saliency-Regularized Deep Multi-Task Learning

1 code implementation3 Jul 2022 Guangji Bai, Liang Zhao

Specifically, we propose to model the task relation as the similarity between task input gradients, with a theoretical analysis of their equivalency.

Image Classification Multi-Task Learning +1

Controllable Data Generation by Deep Learning: A Review

no code implementations19 Jul 2022 Shiyu Wang, Yuanqi Du, Xiaojie Guo, Bo Pan, Zhaohui Qin, Liang Zhao

This article is a systematic review that explains this promising research area, commonly known as controllable deep data generation.

Speech Synthesis

Two-Stage Fine-Tuning: A Novel Strategy for Learning Class-Imbalanced Data

1 code implementation22 Jul 2022 Taha ValizadehAslani, Yiwen Shi, Jing Wang, Ping Ren, Yi Zhang, Meng Hu, Liang Zhao, Hualou Liang

Owing to this paucity of samples, learning on the tail classes is especially challenging for the fine-tuning when transferring a pretrained model to a downstream task.

text-classification Text Classification

OneEE: A One-Stage Framework for Fast Overlapping and Nested Event Extraction

1 code implementation COLING 2022 Hu Cao, Jingye Li, Fangfang Su, Fei Li, Hao Fei, Shengqiong Wu, Bobo Li, Liang Zhao, Donghong Ji

Event extraction (EE) is an essential task of information extraction, which aims to extract structured event information from unstructured text.

Event Extraction Relation

Molecular Design Based on Integer Programming and Quadratic Descriptors in a Two-layered Model

1 code implementation13 Sep 2022 Jianshen Zhu, Naveed Ahmed Azam, Shengjuan Cao, Ryota Ido, Kazuya Haraguchi, Liang Zhao, Hiroshi Nagamochi, Tatsuya Akutsu

A set of graph theoretical descriptors in the feature function plays a key role to derive a compact formulation of such an MILP.

Multi-objective Deep Data Generation with Correlated Property Control

no code implementations1 Oct 2022 Shiyu Wang, Xiaojie Guo, Xuanyang Lin, Bo Pan, Yuanqi Du, Yinkai Wang, Yanfang Ye, Ashley Ann Petersen, Austin Leitgeb, Saleh AlKhalifa, Kevin Minbiole, William Wuest, Amarda Shehu, Liang Zhao

Developing deep generative models has been an emerging field due to the ability to model and generate complex data for various purposes, such as image synthesis and molecular design.

Image Generation

Deep Spatial Domain Generalization

1 code implementation3 Oct 2022 Dazhou Yu, Guangji Bai, Yun Li, Liang Zhao

Spatial domain generalization is a spatial extension of domain generalization, which can generalize to unseen spatial domains in continuous 2D space.

Domain Generalization Spatial Interpolation

TOE: A Grid-Tagging Discontinuous NER Model Enhanced by Embedding Tag/Word Relations and More Fine-Grained Tags

1 code implementation1 Nov 2022 Jiang Liu, Donghong Ji, Jingye Li, Dongdong Xie, Chong Teng, Liang Zhao, Fei Li

Concretely, we construct tag representations and embed them into TREM, so that TREM can treat tag and word representations as queries/keys/values and utilize self-attention to model their relationships.

named-entity-recognition Named Entity Recognition +2

DeepGAR: Deep Graph Learning for Analogical Reasoning

1 code implementation19 Nov 2022 Chen Ling, Tanmoy Chowdhury, Junji Jiang, Junxiang Wang, Xuchao Zhang, Haifeng Chen, Liang Zhao

As the most well-known computational method of analogical reasoning, Structure-Mapping Theory (SMT) abstracts both target and base subjects into relational graphs and forms the cognitive process of analogical reasoning by finding a corresponding subgraph (i. e., correspondence) in the target graph that is aligned with the base graph.

Graph Learning

Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning

no code implementations7 Dec 2022 Yuyang Gao, Siyi Gu, Junji Jiang, Sungsoo Ray Hong, Dazhou Yu, Liang Zhao

As the societal impact of Deep Neural Networks (DNNs) grows, the goals for advancing DNNs become more complex and diverse, ranging from improving a conventional model accuracy metric to infusing advanced human virtues such as fairness, accountability, transparency (FaccT), and unbiasedness.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +1

Saliency-Augmented Memory Completion for Continual Learning

1 code implementation26 Dec 2022 Guangji Bai, Chen Ling, Yuyang Gao, Liang Zhao

Specifically, we innovatively propose to store the part of the image most important to the tasks in episodic memory by saliency map extraction and memory encoding.

Bilevel Optimization Continual Learning +1

MAGI: Multi-Annotated Explanation-Guided Learning

no code implementations ICCV 2023 Yifei Zhang, Siyi Gu, Yuyang Gao, Bo Pan, Xiaofeng Yang, Liang Zhao

This technique aims to improve the predictability of the model by incorporating human understanding of the prediction process into the training phase.

Variational Inference

Knowledge-enhanced Neural Machine Reasoning: A Review

no code implementations4 Feb 2023 Tanmoy Chowdhury, Chen Ling, Xuchao Zhang, Xujiang Zhao, Guangji Bai, Jian Pei, Haifeng Chen, Liang Zhao

Knowledge-enhanced neural machine reasoning has garnered significant attention as a cutting-edge yet challenging research area with numerous practical applications.

Dealing With Heterogeneous 3D MR Knee Images: A Federated Few-Shot Learning Method With Dual Knowledge Distillation

1 code implementation25 Mar 2023 Xiaoxiao He, Chaowei Tan, Bo Liu, Liping Si, Weiwu Yao, Liang Zhao, Di Liu, Qilong Zhangli, Qi Chang, Kang Li, Dimitris N. Metaxas

The supervised learning of the proposed method extracts features from limited labeled data in each client, while the unsupervised data is used to distill both feature and response-based knowledge from a national data repository to further improve the accuracy of the collaborative model and reduce the communication cost.

Federated Learning Few-Shot Learning +1

Molecular Design Based on Integer Programming and Splitting Data Sets by Hyperplanes

no code implementations27 Apr 2023 Jianshen Zhu, Naveed Ahmed Azam, Kazuya Haraguchi, Liang Zhao, Hiroshi Nagamochi, Tatsuya Akutsu

A novel framework for designing the molecular structure of chemical compounds with a desired chemical property has recently been proposed.

Deep Graph Representation Learning and Optimization for Influence Maximization

1 code implementation1 May 2023 Chen Ling, Junji Jiang, Junxiang Wang, My Thai, Lukas Xue, James Song, Meikang Qiu, Liang Zhao

Influence maximization (IM) is formulated as selecting a set of initial users from a social network to maximize the expected number of influenced users.

Graph Representation Learning

Domain Generalization Deep Graph Transformation

no code implementations19 May 2023 Shiyu Wang, Guangji Bai, Qingyang Zhu, Zhaohui Qin, Liang Zhao

As a result, domain generalization graph transformation that predicts graphs not available in the training data is under-explored, with multiple key challenges to be addressed including (1) the extreme space complexity when training on all input-output mode combinations, (2) difference of graph topologies between the input and the output modes, and (3) how to generalize the model to (unseen) target domains that are not in the training data.

Domain Generalization Link Prediction

Graph Neural Network for spatiotemporal data: methods and applications

no code implementations30 May 2023 Yun Li, Dazhou Yu, Zhenke Liu, Minxing Zhang, Xiaoyun Gong, Liang Zhao

Graph neural networks (GNNs) have emerged as a powerful tool for modeling and understanding data with dependencies to each other such as spatial and temporal dependencies.

Weather Forecasting

JGAT: a joint spatio-temporal graph attention model for brain decoding

1 code implementation3 Jun 2023 Han Yi Chiu, Liang Zhao, Anqi Wu

However, traditional approaches for integrating FC and SC overlook the dynamical variations, which stand a great chance to over-generalize the brain neural network.

Brain Decoding Graph Attention

Leveraging GPT-4 for Food Effect Summarization to Enhance Product-Specific Guidance Development via Iterative Prompting

no code implementations28 Jun 2023 Yiwen Shi, Ping Ren, Jing Wang, Biao Han, Taha ValizadehAslani, Felix Agbavor, Yi Zhang, Meng Hu, Liang Zhao, Hualou Liang

Specifically, we propose a three-turn iterative prompting approach to food effect summarization in which the keyword-focused and length-controlled prompts are respectively provided in consecutive turns to refine the quality of the generated summary.

Text Summarization

Designing a Direct Feedback Loop between Humans and Convolutional Neural Networks through Local Explanations

1 code implementation8 Jul 2023 Tong Steven Sun, Yuyang Gao, Shubham Khaladkar, Sijia Liu, Liang Zhao, Young-Ho Kim, Sungsoo Ray Hong

To mitigate the gap, we designed DeepFuse, the first interactive design that realizes the direct feedback loop between a user and CNNs in diagnosing and revising CNN's vulnerability using local explanations.

Explainable Artificial Intelligence (XAI)

ChatSpot: Bootstrapping Multimodal LLMs via Precise Referring Instruction Tuning

no code implementations18 Jul 2023 Liang Zhao, En Yu, Zheng Ge, Jinrong Yang, Haoran Wei, HongYu Zhou, Jianjian Sun, Yuang Peng, Runpei Dong, Chunrui Han, Xiangyu Zhang

Based on precise referring instruction, we propose ChatSpot, a unified end-to-end multimodal large language model that supports diverse forms of interactivity including mouse clicks, drag-and-drop, and drawing boxes, which provides a more flexible and seamless interactive experience.

Instruction Following Language Modelling +1

Staleness-Alleviated Distributed GNN Training via Online Dynamic-Embedding Prediction

no code implementations25 Aug 2023 Guangji Bai, Ziyang Yu, Zheng Chai, Yue Cheng, Liang Zhao

It utilizes an offline memory to cache historical information (e. g., node embedding) as an affordable approximation of the exact value and achieves high concurrency.

Distributed Computing

Feature Attention Network (FA-Net): A Deep-Learning Based Approach for Underwater Single Image Enhancement

no code implementations30 Aug 2023 Muhammad Hamza, Ammar Hawbani, Sami Ul Rehman, Xingfu Wang, Liang Zhao

In particular, we propose a Residual Feature Attention Block (RFAB), containing the channel attention, pixel attention, and residual learning mechanism with long and short skip connections.

Image Enhancement

Helper Recommendation with seniority control in Online Health Community

no code implementations6 Sep 2023 Junruo Gao, Chen Ling, Carl Yang, Liang Zhao

Online health communities (OHCs) are forums where patients with similar conditions communicate their experiences and provide moral support.

Recommendation Systems

DreamLLM: Synergistic Multimodal Comprehension and Creation

1 code implementation20 Sep 2023 Runpei Dong, Chunrui Han, Yuang Peng, Zekun Qi, Zheng Ge, Jinrong Yang, Liang Zhao, Jianjian Sun, HongYu Zhou, Haoran Wei, Xiangwen Kong, Xiangyu Zhang, Kaisheng Ma, Li Yi

This paper presents DreamLLM, a learning framework that first achieves versatile Multimodal Large Language Models (MLLMs) empowered with frequently overlooked synergy between multimodal comprehension and creation.

 Ranked #1 on Visual Question Answering on MMBench (GPT-3.5 score metric)

multimodal generation Visual Question Answering +2

Multi-Prompt Fine-Tuning of Foundation Models for Enhanced Medical Image Segmentation

no code implementations3 Oct 2023 Xiangru Li, Yifei Zhang, Liang Zhao

The Segment Anything Model (SAM) is a powerful foundation model that introduced revolutionary advancements in natural image segmentation.

Image Segmentation Medical Image Segmentation +2

Saliency-Guided Hidden Associative Replay for Continual Learning

1 code implementation6 Oct 2023 Guangji Bai, Qilong Zhao, Xiaoyang Jiang, Yifei Zhang, Liang Zhao

Continual Learning is a burgeoning domain in next-generation AI, focusing on training neural networks over a sequence of tasks akin to human learning.

Continual Learning Retrieval

Balancing Specialized and General Skills in LLMs: The Impact of Modern Tuning and Data Strategy

no code implementations7 Oct 2023 Zheng Zhang, Chen Zheng, Da Tang, Ke Sun, Yukun Ma, Yingtong Bu, Xun Zhou, Liang Zhao

This paper introduces a multifaceted methodology for fine-tuning and evaluating large language models (LLMs) for specialized monetization tasks.

Large Language Models for Spatial Trajectory Patterns Mining

no code implementations7 Oct 2023 Zheng Zhang, Hossein Amiri, Zhenke Liu, Andreas Züfle, Liang Zhao

Identifying anomalous human spatial trajectory patterns can indicate dynamic changes in mobility behavior with applications in domains like infectious disease monitoring and elderly care.

Anomaly Detection

Transferable Deep Clustering Model

no code implementations7 Oct 2023 Zheng Zhang, Liang Zhao

Deep learning has shown remarkable success in the field of clustering recently.

Clustering Deep Clustering +1

Beyond Text: A Deep Dive into Large Language Models' Ability on Understanding Graph Data

no code implementations7 Oct 2023 Yuntong Hu, Zheng Zhang, Liang Zhao

Large language models (LLMs) have achieved impressive performance on many natural language processing tasks.

Benchmarking

SurroCBM: Concept Bottleneck Surrogate Models for Generative Post-hoc Explanation

no code implementations11 Oct 2023 Bo Pan, Zhenke Liu, Yifei Zhang, Liang Zhao

Explainable AI seeks to bring light to the decision-making processes of black-box models.

Decision Making

Controllable Data Generation Via Iterative Data-Property Mutual Mappings

no code implementations11 Oct 2023 Bo Pan, Muran Qin, Shiyu Wang, Yifei Zhang, Liang Zhao

To address these challenges, in this paper, we propose a general framework to enhance VAE-based data generators with property controllability and ensure disentanglement.

Disentanglement

XAI Benchmark for Visual Explanation

no code implementations12 Oct 2023 Yifei Zhang, Siyi Gu, James Song, Bo Pan, Guangji Bai, Liang Zhao

Our proposed benchmarks facilitate a fair evaluation and comparison of visual explanation methods.

Decision Making Explainable artificial intelligence +2

Visual Attention-Prompted Prediction and Learning

no code implementations12 Oct 2023 Yifei Zhang, Siyi Gu, Bo Pan, Guangji Bai, Xiaofeng Yang, Liang Zhao

To tackle these challenges, we propose a novel framework called Visual Attention-Prompted Prediction and Learning, which seamlessly integrates visual attention prompts into the model's decision-making process and adapts to images both with and without attention prompts for prediction.

Decision Making

Exploring Damping Effect of Inner Control Loops for Grid-Forming VSCs

no code implementations14 Oct 2023 Liang Zhao, Xiongfei Wang, Zheming Jin

This paper presents an analytical approach to explore the damping effect of inner loops on grid-forming converters.

Open-ended Commonsense Reasoning with Unrestricted Answer Scope

no code implementations18 Oct 2023 Chen Ling, Xuchao Zhang, Xujiang Zhao, Yanchi Liu, Wei Cheng, Mika Oishi, Takao Osaki, Katsushi Matsuda, Haifeng Chen, Liang Zhao

In this work, we leverage pre-trained language models to iteratively retrieve reasoning paths on the external knowledge base, which does not require task-specific supervision.

Question Answering Retrieval

SkyMath: Technical Report

1 code implementation25 Oct 2023 Liu Yang, Haihua Yang, Wenjun Cheng, Lei Lin, Chenxia Li, Yifu Chen, Lunan Liu, Jianfei Pan, Tianwen Wei, Biye Li, Liang Zhao, Lijie Wang, Bo Zhu, Guoliang Li, Xuejie Wu, Xilin Luo, Rui Hu

Large language models (LLMs) have shown great potential to solve varieties of natural language processing (NLP) tasks, including mathematical reasoning.

GSM8K Language Modelling +2

Merlin:Empowering Multimodal LLMs with Foresight Minds

no code implementations30 Nov 2023 En Yu, Liang Zhao, Yana Wei, Jinrong Yang, Dongming Wu, Lingyu Kong, Haoran Wei, Tiancai Wang, Zheng Ge, Xiangyu Zhang, Wenbing Tao

Then, FIT requires MLLMs to first predict trajectories of related objects and then reason about potential future events based on them.

Visual Question Answering

Non-Euclidean Spatial Graph Neural Network

1 code implementation17 Dec 2023 Zheng Zhang, Sirui Li, Jingcheng Zhou, Junxiang Wang, Abhinav Angirekula, Allen Zhang, Liang Zhao

Besides, existing spatial network representation learning methods can only consider networks embedded in Euclidean space, and can not well exploit the rich geometric information carried by irregular and non-uniform non-Euclidean space.

Representation Learning

POND: Multi-Source Time Series Domain Adaptation with Information-Aware Prompt Tuning

no code implementations19 Dec 2023 Junxiang Wang, Guangji Bai, Wei Cheng, Zhengzhang Chen, Liang Zhao, Haifeng Chen

In order to tackle these challenges simultaneously, in this paper, we introduce PrOmpt-based domaiN Discrimination (POND), the first framework to utilize prompts for time series domain adaptation.

Domain Adaptation Human Activity Recognition +3

Length Extrapolation of Transformers: A Survey from the Perspective of Position Encoding

no code implementations28 Dec 2023 Liang Zhao, Xiaocheng Feng, Xiachong Feng, Bing Qin, Ting Liu

Transformer has taken the natural language processing (NLP) field by storm since birth, owing to its superior ability to model complex dependencies in sequences.

Position

Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models

1 code implementation1 Jan 2024 Guangji Bai, Zheng Chai, Chen Ling, Shiyu Wang, Jiaying Lu, Nan Zhang, Tingwei Shi, Ziyang Yu, Mengdan Zhu, Yifei Zhang, Carl Yang, Yue Cheng, Liang Zhao

We categorize methods based on their optimization focus: computational, memory, energy, financial, and network resources and their applicability across various stages of an LLM's lifecycle, including architecture design, pretraining, finetuning, and system design.

Gene-associated Disease Discovery Powered by Large Language Models

no code implementations16 Jan 2024 Jiayu Chang, Shiyu Wang, Chen Ling, Zhaohui Qin, Liang Zhao

The intricate relationship between genetic variation and human diseases has been a focal point of medical research, evidenced by the identification of risk genes regarding specific diseases.

Decision Making Retrieval

Small Language Model Meets with Reinforced Vision Vocabulary

no code implementations23 Jan 2024 Haoran Wei, Lingyu Kong, Jinyue Chen, Liang Zhao, Zheng Ge, En Yu, Jianjian Sun, Chunrui Han, Xiangyu Zhang

In Vary-toy, we introduce an improved vision vocabulary, allowing the model to not only possess all features of Vary but also gather more generality.

Language Modelling Large Language Model +3

3DPFIX: Improving Remote Novices' 3D Printing Troubleshooting through Human-AI Collaboration

no code implementations29 Jan 2024 Nahyun Kwon, Tong Sun, Yuyang Gao, Liang Zhao, Xu Wang, Jeeeun Kim, Sungsoo Ray Hong

While troubleshooting plays an essential part of 3D printing, the process remains challenging for many remote novices even with the help of well-developed online sources, such as online troubleshooting archives and online community help.

Explaining latent representations of generative models with large multimodal models

no code implementations2 Feb 2024 Mengdan Zhu, Zhenke Liu, Bo Pan, Abhinav Angirekula, Liang Zhao

Learning interpretable representations of data generative latent factors is an important topic for the development of artificial intelligence.

Disentanglement Explanation Generation

NK Hybrid Genetic Algorithm for Clustering

1 code implementation6 Feb 2024 Renato Tinós, Liang Zhao, Francisco Chicano, Darrell Whitley

Mutation operators, a partition crossover, and a local search strategy are proposed, all using information about the relationship between decision variables.

Clustering

Uncertainty Decomposition and Quantification for In-Context Learning of Large Language Models

1 code implementation15 Feb 2024 Chen Ling, Xujiang Zhao, Wei Cheng, Yanchi Liu, Yiyou Sun, Xuchao Zhang, Mika Oishi, Takao Osaki, Katsushi Matsuda, Jie Ji, Guangji Bai, Liang Zhao, Haifeng Chen

Existing works have been devoted to quantifying the uncertainty in LLM's response, but they often overlook the complex nature of LLMs and the uniqueness of in-context learning.

Hallucination In-Context Learning

A Condensed Transition Graph Framework for Zero-shot Link Prediction with Large Language Models

no code implementations16 Feb 2024 Mingchen Li, Chen Ling, Rui Zhang, Liang Zhao

To address this, in this work, we introduce a Condensed Transition Graph Framework for Zero-Shot Link Prediction (CTLP), which encodes all the paths' information in linear time complexity to predict unseen relations between entities, attaining both efficiency and information preservation.

Contrastive Learning Knowledge Graphs +1

Distilling Large Language Models for Text-Attributed Graph Learning

no code implementations19 Feb 2024 Bo Pan, Zheng Zhang, Yifei Zhang, Yuntong Hu, Liang Zhao

To address the inherent gaps between LLMs (generative models for texts) and graph models (discriminative models for graphs), we propose first to let LLMs teach an interpreter with rich textual rationale and then let a student model mimic the interpreter's reasoning without LLMs' textual rationale.

Graph Learning TAG

ELAD: Explanation-Guided Large Language Models Active Distillation

no code implementations20 Feb 2024 Yifei Zhang, Bo Pan, Chen Ling, Yuntong Hu, Liang Zhao

The deployment and application of Large Language Models (LLMs) is hindered by their memory inefficiency, computational demands, and the high costs of API inferences.

Active Learning Knowledge Distillation

MIM-Reasoner: Learning with Theoretical Guarantees for Multiplex Influence Maximization

1 code implementation24 Feb 2024 Nguyen Do, Tanmoy Chowdhury, Chen Ling, Liang Zhao, My T. Thai

Multiplex influence maximization (MIM) asks us to identify a set of seed users such as to maximize the expected number of influenced users in a multiplex network.

Gradient-Free Adaptive Global Pruning for Pre-trained Language Models

1 code implementation28 Feb 2024 Guangji Bai, Yijiang Li, Chen Ling, Kibaek Kim, Liang Zhao

The transformative impact of large language models (LLMs) like LLaMA and GPT on natural language processing is countered by their prohibitive computational demands.

Computational Efficiency Problem Decomposition

DUE: Dynamic Uncertainty-Aware Explanation Supervision via 3D Imputation

no code implementations16 Mar 2024 Qilong Zhao, Yifei Zhang, Mengdan Zhu, Siyi Gu, Yuyang Gao, Xiaofeng Yang, Liang Zhao

Explanation supervision aims to enhance deep learning models by integrating additional signals to guide the generation of model explanations, showcasing notable improvements in both the predictability and explainability of the model.

Imputation

The ManDi Corpus: A Spoken Corpus of Mandarin Regional Dialects

no code implementations LREC 2022 Liang Zhao, Eleanor Chodroff

In the present paper, we introduce the ManDi Corpus, a spoken corpus of regional Mandarin dialects and Standard Mandarin.

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