Search Results for author: Qi Zhu

Found 91 papers, 39 papers with code

When does Further Pre-training MLM Help? An Empirical Study on Task-Oriented Dialog Pre-training

1 code implementation EMNLP (insights) 2021 Qi Zhu, Yuxian Gu, Lingxiao Luo, Bing Li, Cheng Li, Wei Peng, Minlie Huang, Xiaoyan Zhu

Further pre-training language models on in-domain data (domain-adaptive pre-training, DAPT) or task-relevant data (task-adaptive pre-training, TAPT) before fine-tuning has been shown to improve downstream tasks’ performances.

Can GNN be Good Adapter for LLMs?

no code implementations20 Feb 2024 Xuanwen Huang, Kaiqiao Han, Yang Yang, Dezheng Bao, Quanjin Tao, Ziwei Chai, Qi Zhu

In terms of efficiency, the GNN adapter introduces only a few trainable parameters and can be trained with low computation costs.

Node Classification Recommendation Systems +2

Phase-driven Domain Generalizable Learning for Nonstationary Time Series

no code implementations5 Feb 2024 Payal Mohapatra, Lixu Wang, Qi Zhu

Monitoring and recognizing patterns in continuous sensing data is crucial for many practical applications.

Gesture Recognition Human Activity Recognition +2

Boosting Long-Delayed Reinforcement Learning with Auxiliary Short-Delayed Task

no code implementations5 Feb 2024 Qingyuan Wu, Simon Sinong Zhan, YiXuan Wang, Chung-Wei Lin, Chen Lv, Qi Zhu, Chao Huang

Reinforcement learning is challenging in delayed scenarios, a common real-world situation where observations and interactions occur with delays.


Federated Learning with New Knowledge: Fundamentals, Advances, and Futures

1 code implementation3 Feb 2024 Lixu Wang, Yang Zhao, Jiahua Dong, Ating Yin, Qinbin Li, Xiao Wang, Dusit Niyato, Qi Zhu

Federated Learning (FL) is a privacy-preserving distributed learning approach that is rapidly developing in an era where privacy protection is increasingly valued.

Federated Learning Privacy Preserving

Planning, Creation, Usage: Benchmarking LLMs for Comprehensive Tool Utilization in Real-World Complex Scenarios

1 code implementation30 Jan 2024 Shijue Huang, Wanjun Zhong, Jianqiao Lu, Qi Zhu, Jiahui Gao, Weiwen Liu, Yutai Hou, Xingshan Zeng, Yasheng Wang, Lifeng Shang, Xin Jiang, Ruifeng Xu, Qun Liu

The recent trend of using Large Language Models (LLMs) as tool agents in real-world applications underscores the necessity for comprehensive evaluations of their capabilities, particularly in complex scenarios involving planning, creating, and using tools.


YODA: Teacher-Student Progressive Learning for Language Models

no code implementations28 Jan 2024 Jianqiao Lu, Wanjun Zhong, YuFei Wang, Zhijiang Guo, Qi Zhu, Wenyong Huang, Yanlin Wang, Fei Mi, Baojun Wang, Yasheng Wang, Lifeng Shang, Xin Jiang, Qun Liu

With the teacher's guidance, the student learns to iteratively refine its answer with feedback, and forms a robust and comprehensive understanding of the posed questions.

GSM8K Math

DACR: Distribution-Augmented Contrastive Reconstruction for Time-Series Anomaly Detection

no code implementations20 Jan 2024 Lixu Wang, Shichao Xu, Xinyu Du, Qi Zhu

Anomaly detection in time-series data is crucial for identifying faults, failures, threats, and outliers across a range of applications.

Anomaly Detection Contrastive Learning +2

Fine-tuning Graph Neural Networks by Preserving Graph Generative Patterns

1 code implementation21 Dec 2023 Yifei Sun, Qi Zhu, Yang Yang, Chunping Wang, Tianyu Fan, Jiajun Zhu, Lei Chen

In this paper, we identify the fundamental cause of structural divergence as the discrepancy of generative patterns between the pre-training and downstream graphs.

Graph Mining Transfer Learning

Federated Continual Novel Class Learning

no code implementations21 Dec 2023 Lixu Wang, Chenxi Liu, Junfeng Guo, Jiahua Dong, Xiao Wang, Heng Huang, Qi Zhu

In a privacy-focused era, Federated Learning (FL) has emerged as a promising machine learning technique.

Federated Learning Novel Class Discovery +1

Empowering Autonomous Driving with Large Language Models: A Safety Perspective

no code implementations28 Nov 2023 YiXuan Wang, Ruochen Jiao, Chengtian Lang, Sinong Simon Zhan, Chao Huang, Zhaoran Wang, Zhuoran Yang, Qi Zhu

Autonomous Driving (AD) faces crucial hurdles for commercial launch, notably in the form of diminished public trust and safety concerns from long-tail unforeseen driving scenarios.

Autonomous Driving Common Sense Reasoning

State-Wise Safe Reinforcement Learning With Pixel Observations

1 code implementation3 Nov 2023 Simon Sinong Zhan, YiXuan Wang, Qingyuan Wu, Ruochen Jiao, Chao Huang, Qi Zhu

In the context of safe exploration, Reinforcement Learning (RL) has long grappled with the challenges of balancing the tradeoff between maximizing rewards and minimizing safety violations, particularly in complex environments with contact-rich or non-smooth dynamics, and when dealing with high-dimensional pixel observations.

reinforcement-learning Reinforcement Learning (RL) +2

SELF: Self-Evolution with Language Feedback

no code implementations1 Oct 2023 Jianqiao Lu, Wanjun Zhong, Wenyong Huang, YuFei Wang, Qi Zhu, Fei Mi, Baojun Wang, Weichao Wang, Xingshan Zeng, Lifeng Shang, Xin Jiang, Qun Liu

SELF initiates with a meta-skill learning process that equips the LLMs with capabilities for self-feedback and self-refinement.

Language Modelling Large Language Model

Parallelizing non-linear sequential models over the sequence length

1 code implementation21 Sep 2023 Yi Heng Lim, Qi Zhu, Joshua Selfridge, Muhammad Firmansyah Kasim

Sequential models, such as Recurrent Neural Networks and Neural Ordinary Differential Equations, have long suffered from slow training due to their inherent sequential nature.

Time Series Time Series Classification

Cramer-Rao Bound Optimization for Active RIS-Empowered ISAC Systems

no code implementations17 Sep 2023 Qi Zhu, Ming Li, Rang Liu, Qian Liu

In this paper, we investigate the deployment of active RIS-empowered ISAC systems to enhance radar echo signal quality as well as communication performance.

Kinematics-aware Trajectory Generation and Prediction with Latent Stochastic Differential Modeling

no code implementations17 Sep 2023 Ruochen Jiao, YiXuan Wang, Xiangguo Liu, Chao Huang, Qi Zhu

Trajectory generation and trajectory prediction are two critical tasks for autonomous vehicles, which generate various trajectories during development and predict the trajectories of surrounding vehicles during operation, respectively.

Autonomous Vehicles Trajectory Prediction

PolyGET: Accelerating Polymer Simulations by Accurate and Generalizable Forcefield with Equivariant Transformer

no code implementations1 Sep 2023 Rui Feng, Huan Tran, Aubrey Toland, Binghong Chen, Qi Zhu, Rampi Ramprasad, Chao Zhang

Machine learning (ML) forcefields have been developed to achieve both the accuracy of ab initio methods and the efficiency of empirical force fields.

Collaborative Multi-Agent Video Fast-Forwarding

no code implementations27 May 2023 Shuyue Lan, Zhilu Wang, Ermin Wei, Amit K. Roy-Chowdhury, Qi Zhu

We show that compared with other approaches in the literature, our frameworks achieve better coverage of important frames, while significantly reducing the number of frames processed at each agent.

Patton: Language Model Pretraining on Text-Rich Networks

no code implementations20 May 2023 Bowen Jin, Wentao Zhang, Yu Zhang, Yu Meng, Xinyang Zhang, Qi Zhu, Jiawei Han

A real-world text corpus sometimes comprises not only text documents but also semantic links between them (e. g., academic papers in a bibliographic network are linked by citations and co-authorships).

Language Modelling Masked Language Modeling +1

Robot-Enabled Construction Assembly with Automated Sequence Planning based on ChatGPT: RoboGPT

no code implementations21 Apr 2023 Hengxu You, Yang Ye, Tianyu Zhou, Qi Zhu, Jing Du

To expand the ability of the current robot system in sequential understanding, this paper introduces RoboGPT, a novel system that leverages the advanced reasoning capabilities of ChatGPT, a large language model, for automated sequence planning in robot-based assembly applied to construction tasks.

Language Modelling Large Language Model

POLAR-Express: Efficient and Precise Formal Reachability Analysis of Neural-Network Controlled Systems

1 code implementation31 Mar 2023 YiXuan Wang, Weichao Zhou, Jiameng Fan, Zhilu Wang, Jiajun Li, Xin Chen, Chao Huang, Wenchao Li, Qi Zhu

We also present a novel approach to propagate TMs more efficiently and precisely across ReLU activation functions.

Leapfrog Diffusion Model for Stochastic Trajectory Prediction

1 code implementation CVPR 2023 Weibo Mao, Chenxin Xu, Qi Zhu, Siheng Chen, Yanfeng Wang

The core of the proposed LED is to leverage a trainable leapfrog initializer to directly learn an expressive multi-modal distribution of future trajectories, which skips a large number of denoising steps, significantly accelerating inference speed.

Denoising Trajectory Prediction

Learning Representation for Anomaly Detection of Vehicle Trajectories

no code implementations9 Mar 2023 Ruochen Jiao, Juyang Bai, Xiangguo Liu, Takami Sato, Xiaowei Yuan, Qi Alfred Chen, Qi Zhu

We conduct extensive experiments to demonstrate that our supervised method based on contrastive learning and unsupervised method based on reconstruction with semantic latent space can significantly improve the performance of anomalous trajectory detection in their corresponding settings over various baseline methods.

Anomaly Detection Autonomous Driving +3

Joint Transceiver Beamforming and Reflecting Design for Active RIS-Aided ISAC Systems

no code implementations21 Feb 2023 Qi Zhu, Ming Li, Rang Liu, Qian Liu

Integrated sensing and communication (ISAC) is recognized as a promising technology with great potential in saving hardware and spectrum resources, since it simultaneously realizes radar detection and user communication functions in the fully-shared platform.

The Effect of Metadata on Scientific Literature Tagging: A Cross-Field Cross-Model Study

1 code implementation7 Feb 2023 Yu Zhang, Bowen Jin, Qi Zhu, Yu Meng, Jiawei Han

Due to the exponential growth of scientific publications on the Web, there is a pressing need to tag each paper with fine-grained topics so that researchers can track their interested fields of study rather than drowning in the whole literature.

Language Modelling Multi Label Text Classification +3

DEJA VU: Continual Model Generalization For Unseen Domains

2 code implementations25 Jan 2023 Chenxi Liu, Lixu Wang, Lingjuan Lyu, Chen Sun, Xiao Wang, Qi Zhu

To overcome these limitations of DA and DG in handling the Unfamiliar Period during continual domain shift, we propose RaTP, a framework that focuses on improving models' target domain generalization (TDG) capability, while also achieving effective target domain adaptation (TDA) capability right after training on certain domains and forgetting alleviation (FA) capability on past domains.

Data Augmentation Domain Generalization

Rectified Pessimistic-Optimistic Learning for Stochastic Continuum-armed Bandit with Constraints

no code implementations27 Nov 2022 Hengquan Guo, Qi Zhu, Xin Liu

This paper studies the problem of stochastic continuum-armed bandit with constraints (SCBwC), where we optimize a black-box reward function $f(x)$ subject to a black-box constraint function $g(x)\leq 0$ over a continuous space $\mathcal X$.

Gaussian Processes

Single-Pass Contrastive Learning Can Work for Both Homophilic and Heterophilic Graph

1 code implementation20 Nov 2022 Haonan Wang, Jieyu Zhang, Qi Zhu, Wei Huang, Kenji Kawaguchi, Xiaokui Xiao

To answer this question, we theoretically study the concentration property of features obtained by neighborhood aggregation on homophilic and heterophilic graphs, introduce the single-pass augmentation-free graph contrastive learning loss based on the property, and provide performance guarantees for the minimizer of the loss on downstream tasks.

Contrastive Learning

Adversarial and Random Transformations for Robust Domain Adaptation and Generalization

no code implementations13 Nov 2022 Liang Xiao, Jiaolong Xu, Dawei Zhao, Erke Shang, Qi Zhu, Bin Dai

In this work, we show that by simply applying consistency training with random data augmentation, state-of-the-art results on domain adaptation (DA) and generalization (DG) can be obtained.

Data Augmentation Domain Adaptation

Joint Beamforming Designs for Active Reconfigurable Intelligent Surface: A Sub-Connected Array Architecture

no code implementations5 Oct 2022 Qi Zhu, Ming Li, Rang Liu, Yang Liu, Qian Liu

Affected by the "double fading" effect, however, conventional passive RIS cannot bring considerable performance improvement when users are not close enough to RIS.

Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments

no code implementations29 Sep 2022 YiXuan Wang, Simon Sinong Zhan, Ruochen Jiao, Zhilu Wang, Wanxin Jin, Zhuoran Yang, Zhaoran Wang, Chao Huang, Qi Zhu

It is quite challenging to ensure the safety of reinforcement learning (RL) agents in an unknown and stochastic environment under hard constraints that require the system state not to reach certain specified unsafe regions.

Reinforcement Learning (RL) Safe Reinforcement Learning

A Tool for Neural Network Global Robustness Certification and Training

no code implementations15 Aug 2022 Zhilu Wang, YiXuan Wang, Feisi Fu, Ruochen Jiao, Chao Huang, Wenchao Li, Qi Zhu

Moreover, GROCET provides differentiable global robustness, which is leveraged in the training of globally robust neural networks.

Source-Free Domain Adaptation for Real-world Image Dehazing

no code implementations14 Jul 2022 Hu Yu, Jie Huang, Yajing Liu, Qi Zhu, Man Zhou, Feng Zhao

Although certain Domain Adaptation (DA) dehazing methods have been presented, they inevitably require access to the source dataset to reduce the gap between the source synthetic and target real domains.

Image Dehazing Source-Free Domain Adaptation +1

GEMv2: Multilingual NLG Benchmarking in a Single Line of Code

no code implementations22 Jun 2022 Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina McMillan-Major, Anna Shvets, Ashish Upadhyay, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez-Beltrachini, Leonardo F. R. Ribeiro, Lewis Tunstall, Li Zhang, Mahima Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou

This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.

Benchmarking Text Generation

Semi-supervised Semantics-guided Adversarial Training for Trajectory Prediction

no code implementations27 May 2022 Ruochen Jiao, Xiangguo Liu, Takami Sato, Qi Alfred Chen, Qi Zhu

In addition, experiments show that our method can significantly improve the system's robust generalization to unseen patterns of attacks.

Adversarial Robustness Decision Making +1

Heterformer: Transformer-based Deep Node Representation Learning on Heterogeneous Text-Rich Networks

1 code implementation20 May 2022 Bowen Jin, Yu Zhang, Qi Zhu, Jiawei Han

In heterogeneous text-rich networks, this task is more challenging due to (1) presence or absence of text: Some nodes are associated with rich textual information, while others are not; (2) diversity of types: Nodes and edges of multiple types form a heterogeneous network structure.

Clustering Graph Attention +5

Augmentation-Free Graph Contrastive Learning with Performance Guarantee

no code implementations11 Apr 2022 Haonan Wang, Jieyu Zhang, Qi Zhu, Wei Huang

Graph contrastive learning (GCL) is the most representative and prevalent self-supervised learning approach for graph-structured data.

Contrastive Learning Self-Supervised Learning

Efficient Global Robustness Certification of Neural Networks via Interleaving Twin-Network Encoding

no code implementations26 Mar 2022 Zhilu Wang, Chao Huang, Qi Zhu

The robustness of deep neural networks has received significant interest recently, especially when being deployed in safety-critical systems, as it is important to analyze how sensitive the model output is under input perturbations.

Federated Class-Incremental Learning

1 code implementation CVPR 2022 Jiahua Dong, Lixu Wang, Zhen Fang, Gan Sun, Shichao Xu, Xiao Wang, Qi Zhu

It makes the global model suffer from significant catastrophic forgetting on old classes in real-world scenarios, where local clients often collect new classes continuously and have very limited storage memory to store old classes.

Class Incremental Learning Federated Learning +1

Continual Prompt Tuning for Dialog State Tracking

1 code implementation ACL 2022 Qi Zhu, Bing Li, Fei Mi, Xiaoyan Zhu, Minlie Huang

A desirable dialog system should be able to continually learn new skills without forgetting old ones, and thereby adapt to new domains or tasks in its life cycle.

Continual Learning dialog state tracking +1

Shift-Robust Node Classification via Graph Adversarial Clustering

no code implementations7 Mar 2022 Qi Zhu, Chao Zhang, Chanyoung Park, Carl Yang, Jiawei Han

Then a shift-robust classifier is optimized on training graph and adversarial samples on target graph, which are generated by cluster GNN.

Classification Clustering +2

TAE: A Semi-supervised Controllable Behavior-aware Trajectory Generator and Predictor

no code implementations2 Mar 2022 Ruochen Jiao, Xiangguo Liu, Bowen Zheng, Dave Liang, Qi Zhu

Our model addresses trajectory generation and prediction in a unified architecture and benefits both tasks: the model can generate diverse, controllable and realistic trajectories to enhance planner optimization in safety-critical and long-tailed scenarios, and it can provide prediction of critical behavior in addition to the final trajectories for decision making.

Decision Making

Joint Differentiable Optimization and Verification for Certified Reinforcement Learning

no code implementations28 Jan 2022 YiXuan Wang, Simon Zhan, Zhilu Wang, Chao Huang, Zhaoran Wang, Zhuoran Yang, Qi Zhu

In model-based reinforcement learning for safety-critical control systems, it is important to formally certify system properties (e. g., safety, stability) under the learned controller.

Bilevel Optimization Model-based Reinforcement Learning +2

Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data

1 code implementation NeurIPS 2021 Qi Zhu, Natalia Ponomareva, Jiawei Han, Bryan Perozzi

In this work we present a method, Shift-Robust GNN (SR-GNN), designed to account for distributional differences between biased training data and the graph's true inference distribution.

Co-designing Intelligent Control of Building HVACs and Microgrids

1 code implementation18 Jul 2021 Rumia Masburah, Sayan Sinha, Rajib Lochan Jana, Soumyajit Dey, Qi Zhu

Building loads consume roughly 40% of the energy produced in developed countries, a significant part of which is invested towards building temperature-control infrastructure.

Reinforcement Learning (RL)

Learning-based Framework for Sensor Fault-Tolerant Building HVAC Control with Model-assisted Learning

no code implementations27 Jun 2021 Shichao Xu, Yangyang Fu, YiXuan Wang, Zheng O'Neill, Qi Zhu

As people spend up to 87% of their time indoors, intelligent Heating, Ventilation, and Air Conditioning (HVAC) systems in buildings are essential for maintaining occupant comfort and reducing energy consumption.

POLAR: A Polynomial Arithmetic Framework for Verifying Neural-Network Controlled Systems

2 code implementations25 Jun 2021 Chao Huang, Jiameng Fan, Zhilu Wang, YiXuan Wang, Weichao Zhou, Jiajun Li, Xin Chen, Wenchao Li, Qi Zhu

We present POLAR, a polynomial arithmetic-based framework for efficient bounded-time reachability analysis of neural-network controlled systems (NNCSs).

Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorization

1 code implementation ICLR 2022 Lixu Wang, Shichao Xu, Ruiqi Xu, Xiao Wang, Qi Zhu

Our NTL-based authorization approach instead provides data-centric protection, which we call applicability authorization, by significantly degrading the performance of the model on unauthorized data.

Verification in the Loop: Correct-by-Construction Control Learning with Reach-avoid Guarantees

no code implementations6 Jun 2021 YiXuan Wang, Chao Huang, Zhaoran Wang, Zhilu Wang, Qi Zhu

Specifically, we leverage the verification results (computed reachable set of the system state) to construct feedback metrics for control learning, which measure how likely the current design of control parameters can meet the required reach-avoid property for safety and goal-reaching.

Chop Chop BERT: Visual Question Answering by Chopping VisualBERT's Heads

no code implementations30 Apr 2021 Chenyu Gao, Qi Zhu, Peng Wang, Qi Wu

Based on this observation, we design a dynamic chopping module that can automatically remove heads and layers of the VisualBERT at an instance level when dealing with different questions.

Question Answering Visual Question Answering +1

End-to-end Uncertainty-based Mitigation of Adversarial Attacks to Automated Lane Centering

no code implementations27 Feb 2021 Ruochen Jiao, Hengyi Liang, Takami Sato, Junjie Shen, Qi Alfred Chen, Qi Zhu

The experiment results demonstrate that our approach can effectively mitigate the impact of adversarial attacks and can achieve 55% to 90% improvement over the original OpenPilot.

Autonomous Driving

Securing Connected Vehicle Applications with an Efficient Dual Cyber-Physical Blockchain Framework

no code implementations15 Feb 2021 Xiangguo Liu, Baiting Luo, Ahmed Abdo, Nael Abu-Ghazaleh, Qi Zhu

While connected vehicle (CV) applications have the potential to revolutionize traditional transportation system, cyber and physical attacks on them could be devastating.

Cryptography and Security

Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps

1 code implementation9 Dec 2020 Qi Zhu, Chenyu Gao, Peng Wang, Qi Wu

Texts appearing in daily scenes that can be recognized by OCR (Optical Character Recognition) tools contain significant information, such as street name, product brand and prices.

Image Captioning Optical Character Recognition +3

Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization

1 code implementation NeurIPS 2021 Qi Zhu, Carl Yang, Yidan Xu, Haonan Wang, Chao Zhang, Jiawei Han

Graph neural networks (GNNs) have achieved superior performance in various applications, but training dedicated GNNs can be costly for large-scale graphs.

Knowledge Graphs Transfer Learning

Addressing Class Imbalance in Federated Learning

2 code implementations14 Aug 2020 Lixu Wang, Shichao Xu, Xiao Wang, Qi Zhu

Our experiments demonstrate the importance of acknowledging class imbalance and taking measures as early as possible in FL training, and the effectiveness of our method in mitigating the impact.

Federated Learning

Distributed Multi-agent Video Fast-forwarding

1 code implementation10 Aug 2020 Shuyue Lan, Zhilu Wang, Amit K. Roy-Chowdhury, Ermin Wei, Qi Zhu

In many intelligent systems, a network of agents collaboratively perceives the environment for better and more efficient situation awareness.

One for Many: Transfer Learning for Building HVAC Control

no code implementations9 Aug 2020 Shichao Xu, Yi-Xuan Wang, Yanzhi Wang, Zheng O'Neill, Qi Zhu

Traditional HVAC control methods are typically based on creating explicit physical models for building thermal dynamics, which often require significant effort to develop and are difficult to achieve sufficient accuracy and efficiency for runtime building control and scalability for field implementations.

Transfer Learning

Unsupervised Differentiable Multi-aspect Network Embedding

1 code implementation7 Jun 2020 Chanyoung Park, Carl Yang, Qi Zhu, Donghyun Kim, Hwanjo Yu, Jiawei Han

To capture the multiple aspects of each node, existing studies mainly rely on offline graph clustering performed prior to the actual embedding, which results in the cluster membership of each node (i. e., node aspect distribution) fixed throughout training of the embedding model.

Clustering Graph Clustering +2

Structured Multimodal Attentions for TextVQA

2 code implementations1 Jun 2020 Chenyu Gao, Qi Zhu, Peng Wang, Hui Li, Yuliang Liu, Anton Van Den Hengel, Qi Wu

In this paper, we propose an end-to-end structured multimodal attention (SMA) neural network to mainly solve the first two issues above.

Graph Attention Optical Character Recognition (OCR) +3

Recent Advances and Challenges in Task-oriented Dialog System

no code implementations17 Mar 2020 Zheng Zhang, Ryuichi Takanobu, Qi Zhu, Minlie Huang, Xiaoyan Zhu

Due to the significance and value in human-computer interaction and natural language processing, task-oriented dialog systems are attracting more and more attention in both academic and industrial communities.

CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset

2 code implementations TACL 2020 Qi Zhu, Kaili Huang, Zheng Zhang, Xiaoyan Zhu, Minlie Huang

To advance multi-domain (cross-domain) dialogue modeling as well as alleviate the shortage of Chinese task-oriented datasets, we propose CrossWOZ, the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset.

Dialogue State Tracking Task-Oriented Dialogue Systems +1

ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems

1 code implementation ACL 2020 Qi Zhu, Zheng Zhang, Yan Fang, Xiang Li, Ryuichi Takanobu, Jinchao Li, Baolin Peng, Jianfeng Gao, Xiaoyan Zhu, Minlie Huang

We present ConvLab-2, an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models, perform an end-to-end evaluation, and diagnose the weakness of systems.

Task-Oriented Dialogue Systems

Eavesdrop the Composition Proportion of Training Labels in Federated Learning

no code implementations14 Oct 2019 Lixu Wang, Shichao Xu, Xiao Wang, Qi Zhu

Federated learning (FL) has recently emerged as a new form of collaborative machine learning, where a common model can be learned while keeping all the training data on local devices.

Federated Learning Inference Attack

Discovering Hypernymy in Text-Rich Heterogeneous Information Network by Exploiting Context Granularity

1 code implementation4 Sep 2019 Yu Shi, Jiaming Shen, Yuchen Li, Naijing Zhang, Xinwei He, Zhengzhi Lou, Qi Zhu, Matthew Walker, Myunghwan Kim, Jiawei Han

Extensive experiments on two large real-world datasets demonstrate the effectiveness of HyperMine and the utility of modeling context granularity.

Knowledge Graphs

Facet-Aware Evaluation for Extractive Summarization

1 code implementation ACL 2020 Yuning Mao, Liyuan Liu, Qi Zhu, Xiang Ren, Jiawei Han

In this paper, we present a facet-aware evaluation setup for better assessment of the information coverage in extracted summaries.

Extractive Summarization Sentence +1

ReachNN: Reachability Analysis of Neural-Network Controlled Systems

1 code implementation25 Jun 2019 Chao Huang, Jiameng Fan, Wenchao Li, Xin Chen, Qi Zhu

In this work, we propose a new reachability analysis approach based on Bernstein polynomials that can verify neural-network controlled systems with a more general form of activation functions, i. e., as long as they ensure that the neural networks are Lipschitz continuous.

Task-Guided Pair Embedding in Heterogeneous Network

1 code implementation4 Jun 2019 Chanyoung Park, Donghyun Kim, Qi Zhu, Jiawei Han, Hwanjo Yu

In this paper, we propose a novel task-guided pair embedding framework in heterogeneous network, called TaPEm, that directly models the relationship between a pair of nodes that are related to a specific task (e. g., paper-author relationship in author identification).

Network Embedding

ConvLab: Multi-Domain End-to-End Dialog System Platform

2 code implementations ACL 2019 Sungjin Lee, Qi Zhu, Ryuichi Takanobu, Xiang Li, Yaoqin Zhang, Zheng Zhang, Jinchao Li, Baolin Peng, Xiujun Li, Minlie Huang, Jianfeng Gao

We present ConvLab, an open-source multi-domain end-to-end dialog system platform, that enables researchers to quickly set up experiments with reusable components and compare a large set of different approaches, ranging from conventional pipeline systems to end-to-end neural models, in common environments.

Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks

1 code implementation10 Jul 2018 Yu Shi, Qi Zhu, Fang Guo, Chao Zhang, Jiawei Han

To cope with the challenges in the comprehensive transcription of HINs, we propose the HEER algorithm, which embeds HINs via edge representations that are further coupled with properly-learned heterogeneous metrics.

Feature Engineering Network Embedding

FFNet: Video Fast-Forwarding via Reinforcement Learning

1 code implementation CVPR 2018 Shuyue Lan, Rameswar Panda, Qi Zhu, Amit K. Roy-Chowdhury

The first group is supported by video summarization techniques, which require processing of the entire video to select an important subset for showing to users.

reinforcement-learning Reinforcement Learning (RL) +1

Integrating Local Context and Global Cohesiveness for Open Information Extraction

1 code implementation26 Apr 2018 Qi Zhu, Xiang Ren, Jingbo Shang, Yu Zhang, Ahmed El-Kishky, Jiawei Han

However, current Open IE systems focus on modeling local context information in a sentence to extract relation tuples, while ignoring the fact that global statistics in a large corpus can be collectively leveraged to identify high-quality sentence-level extractions.

Open Information Extraction Relation +1

Expert Finding in Heterogeneous Bibliographic Networks with Locally-trained Embeddings

no code implementations9 Mar 2018 Huan Gui, Qi Zhu, Liyuan Liu, Aston Zhang, Jiawei Han

We study the task of expert finding in heterogeneous bibliographical networks based on two aspects: textual content analysis and authority ranking.

AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks

no code implementations5 Mar 2018 Yu Shi, Huan Gui, Qi Zhu, Lance Kaplan, Jiawei Han

Therefore, we are motivated to propose a novel embedding learning framework---AspEm---to preserve the semantic information in HINs based on multiple aspects.

Link Prediction Network Embedding

Deep Reinforcement Learning: Framework, Applications, and Embedded Implementations

no code implementations10 Oct 2017 Hongjia Li, Tianshu Wei, Ao Ren, Qi Zhu, Yanzhi Wang

The recent breakthroughs of deep reinforcement learning (DRL) technique in Alpha Go and playing Atari have set a good example in handling large state and actions spaces of complicated control problems.

Cloud Computing Q-Learning +3

Heterogeneous Supervision for Relation Extraction: A Representation Learning Approach

1 code implementation EMNLP 2017 Liyuan Liu, Xiang Ren, Qi Zhu, Shi Zhi, Huan Gui, Heng Ji, Jiawei Han

These annotations, referred as heterogeneous supervision, often conflict with each other, which brings a new challenge to the original relation extraction task: how to infer the true label from noisy labels for a given instance.

Relation Relation Extraction +1

Adaptive Algorithm and Platform Selection for Visual Detection and Tracking

no code implementations21 May 2016 Shu Zhang, Qi Zhu, Amit Roy-Chowdhury

In this paper, we focus on this problem and propose a framework to adaptively select the "best" algorithm-parameter combination and the computation platform under performance and cost constraints at design time, and adapt the algorithms at runtime based on real-time inputs.

Pedestrian Detection Test

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