Search Results for author: Liang Zhao

Found 151 papers, 51 papers with code

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.

DreamLLM: Synergistic Multimodal Comprehension and Creation

no code implementations20 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.

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

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

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

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

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)

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

A Survey on Knowledge Graphs for Healthcare: Resources, Applications, and Promises

no code implementations7 Jun 2023 Hejie Cui, Jiaying Lu, Shiyu Wang, ran Xu, Wenjing Ma, Shaojun Yu, Yue Yu, Xuan Kan, Chen Ling, Liang Zhao, Joyce Ho, Fei Wang, Carl Yang

Healthcare knowledge graphs (HKGs) have emerged as a promising tool for organizing medical knowledge in a structured and interpretable way, which provides a comprehensive view of medical concepts and their relationships.

Knowledge Graphs

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

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

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

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

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.

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

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.

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

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

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

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

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

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

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.

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

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

no code implementations22 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

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

Finally, the promising future directions of controllable deep data generation are highlighted and five potential challenges are identified.

Speech Synthesis

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

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.

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.

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.


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

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

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

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-detection Object Detection

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-detection Object Detection

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

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

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

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

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.

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.

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.

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

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

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

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

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

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.

Representation Learning

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

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.

BIG-bench Machine Learning 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

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.

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

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.


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.

General Classification Intrusion Detection

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 +1

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

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 +1

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.

Disentanglement Graph Generation

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

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

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.

BIG-bench Machine Learning Motion Detection +1

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.

Management 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

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.


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

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 BIG-bench Machine Learning +1

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.

Vocal Bursts Valence Prediction

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 reinforcement-learning +1

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 regression

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.

Adversarial Robustness

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 +2

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 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)

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.

BIG-bench Machine Learning

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.

Clustering Community Detection +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 Electroencephalogram (EEG) +2

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

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