Search Results for author: Zheng Chen

Found 48 papers, 8 papers with code

CATAMARAN: A Cross-lingual Long Text Abstractive Summarization Dataset

no code implementations LREC 2022 Zheng Chen, Hongyu Lin

Cross-lingual summarization, which produces the summary in one language from a given source document in another language, could be extremely helpful for humans to obtain information across the world.

Abstractive Text Summarization Cross-Lingual Abstractive Summarization

Recursive Generalization Transformer for Image Super-Resolution

no code implementations11 Mar 2023 Zheng Chen, Yulun Zhang, Jinjin Gu, Linghe Kong, Xiaokang Yang

In this work, we propose the Recursive Generalization Transformer (RGT) for image SR, which can capture global spatial information and is suitable for high-resolution images.

Image Reconstruction Image Super-Resolution

IDA: Informed Domain Adaptive Semantic Segmentation

no code implementations5 Mar 2023 Zheng Chen, Zhengming Ding, Jason M. Gregory, Lantao Liu

To improve the UDA-SS performance, we propose an Informed Domain Adaptation (IDA) model, a self-training framework that mixes the data based on class-level segmentation performance, which aims to emphasize small-region semantics during mixup.

Data Augmentation Domain Adaptation +1

SePaint: Semantic Map Inpainting via Multinomial Diffusion

no code implementations5 Mar 2023 Zheng Chen, Deepak Duggirala, David Crandall, Lei Jiang, Lantao Liu

Prediction beyond partial observations is crucial for robots to navigate in unknown environments because it can provide extra information regarding the surroundings beyond the current sensing range or resolution.


Drugs Resistance Analysis from Scarce Health Records via Multi-task Graph Representation

no code implementations22 Feb 2023 Honglin Shu, Pei Gao, Lingwei Zhu, Zheng Chen

In this paper, we propose a novel framework for rapid clinical intervention by viewing health records as graphs whose nodes are mapped from medical events and edges as correspondence between events in given a time window.

Multi-Task Learning

Generalized Munchausen Reinforcement Learning using Tsallis KL Divergence

no code implementations27 Jan 2023 Lingwei Zhu, Zheng Chen, Takamitsu Matsubara, Martha White

Many policy optimization approaches in reinforcement learning incorporate a Kullback-Leilbler (KL) divergence to the previous policy, to prevent the policy from changing too quickly.

Atari Games reinforcement-learning +1

Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networks

no code implementations14 Dec 2022 Chung-Hsuan Hu, Zheng Chen, Erik G. Larsson

Federated Learning (FL) is a collaborative machine learning (ML) framework that combines on-device training and server-based aggregation to train a common ML model among distributed agents.

Federated Learning Scheduling

Cross Aggregation Transformer for Image Restoration

1 code implementation24 Nov 2022 Zheng Chen, Yulun Zhang, Jinjin Gu, Yongbing Zhang, Linghe Kong, Xin Yuan

The core of our CAT is the Rectangle-Window Self-Attention (Rwin-SA), which utilizes horizontal and vertical rectangle window attention in different heads parallelly to expand the attention area and aggregate the features cross different windows.

Image Restoration Inductive Bias

Over-the-Air Federated Learning with Privacy Protection via Correlated Additive Perturbations

no code implementations5 Oct 2022 Jialing Liao, Zheng Chen, Erik G. Larsson

In this work, we aim at minimizing privacy leakage to the adversary and the degradation of model accuracy at the edge server at the same time.

Federated Learning

Cancer Subtyping by Improved Transcriptomic Features Using Vector Quantized Variational Autoencoder

no code implementations20 Jul 2022 Zheng Chen, Ziwei Yang, Lingwei Zhu, Guang Shi, Kun Yue, Takashi Matsubara, Shigehiko Kanaya, MD Altaf-Ul-Amin

As such, existing methods often impose unrealistic assumptions to extract useful features from the data while avoiding overfitting to spurious correlations.

Automated Cancer Subtyping via Vector Quantization Mutual Information Maximization

1 code implementation22 Jun 2022 Zheng Chen, Lingwei Zhu, Ziwei Yang, Takashi Matsubara

Cancer subtyping is crucial for understanding the nature of tumors and providing suitable therapy.


Enforcing KL Regularization in General Tsallis Entropy Reinforcement Learning via Advantage Learning

no code implementations16 May 2022 Lingwei Zhu, Zheng Chen, Eiji Uchibe, Takamitsu Matsubara

Maximum Tsallis entropy (MTE) framework in reinforcement learning has gained popularity recently by virtue of its flexible modeling choices including the widely used Shannon entropy and sparse entropy.

reinforcement-learning Reinforcement Learning (RL)

$q$-Munchausen Reinforcement Learning

no code implementations16 May 2022 Lingwei Zhu, Zheng Chen, Eiji Uchibe, Takamitsu Matsubara

The recently successful Munchausen Reinforcement Learning (M-RL) features implicit Kullback-Leibler (KL) regularization by augmenting the reward function with logarithm of the current stochastic policy.

reinforcement-learning Reinforcement Learning (RL)

Multi-Target Active Object Tracking with Monte Carlo Tree Search and Target Motion Modeling

no code implementations7 May 2022 Zheng Chen, Jian Zhao, Mingyu Yang, Wengang Zhou, Houqiang Li

In this work, we are dedicated to multi-target active object tracking (AOT), where there are multiple targets as well as multiple cameras in the environment.

Multi-agent Reinforcement Learning Object Tracking

Multi-Tier Platform for Cognizing Massive Electroencephalogram

no code implementations21 Apr 2022 Zheng Chen, Lingwei Zhu, Ziwei Yang, Renyuan Zhang

A spiking neural network (SNN) based tier is designed to distill the principle information in terms of spike-streams from the rare features, which maintains the temporal implication in the nature of EEGs.

Electroencephalogram (EEG)

Automated Sleep Staging via Parallel Frequency-Cut Attention

no code implementations7 Apr 2022 Zheng Chen, Ziwei Yang, Lingwei Zhu, Wei Chen, Toshiyo Tamura, Naoaki Ono, MD Altaf-Ul-Amin, Shigehiko Kanaya, Ming Huang

This paper proposes a novel framework for automatically capturing the time-frequency nature of electroencephalogram (EEG) signals of human sleep based on the authoritative sleep medicine guidance.

Decision Making Electroencephalogram (EEG) +2

Adaptive Spike-Like Representation of EEG Signals for Sleep Stages Scoring

no code implementations2 Apr 2022 Lingwei Zhu, Koki Odani, Ziwei Yang, Guang Shi, Yirong Kan, Zheng Chen, Renyuan Zhang

Recently there has seen promising results on automatic stage scoring by extracting spatio-temporal features from electroencephalogram (EEG).

Electroencephalogram (EEG) Feature Engineering

Cancer Subtyping via Embedded Unsupervised Learning on Transcriptomics Data

no code implementations2 Apr 2022 Ziwei Yang, Lingwei Zhu, Zheng Chen, Ming Huang, Naoaki Ono, MD Altaf-Ul-Amin, Shigehiko Kanaya

In this paper, we propose to investigate automatic subtyping from an unsupervised learning perspective by directly constructing the underlying data distribution itself, hence sufficient data can be generated to alleviate the issue of overfitting.


On-Demand AoI Minimization in Resource-Constrained Cache-Enabled IoT Networks with Energy Harvesting Sensors

no code implementations28 Jan 2022 Mohammad Hatami, Markus Leinonen, Zheng Chen, Nikolaos Pappas, Marian Codreanu

We consider a resource-constrained IoT network, where multiple users make on-demand requests to a cache-enabled edge node to send status updates about various random processes, each monitored by an energy harvesting sensor.

Device Scheduling and Update Aggregation Policies for Asynchronous Federated Learning

no code implementations23 Jul 2021 Chung-Hsuan Hu, Zheng Chen, Erik G. Larsson

Federated Learning (FL) is a newly emerged decentralized machine learning (ML) framework that combines on-device local training with server-based model synchronization to train a centralized ML model over distributed nodes.

Federated Learning Scheduling

SPSA-Based Successive Beamforming for Mobile Satellite Receivers with Phased Arrays

no code implementations19 Apr 2021 Zheng Chen, Håkan Johansson

Efficient and low-complexity beamforming design is an important element of satellite communication systems with mobile receivers equipped with phased arrays.

Theoretical analysis on the transient ignition of premixed expanding flame in a quiescent mixture

no code implementations16 Feb 2021 Dehai Yu, Zheng Chen

It is found that as the heating power grows, the memory effect becomes increasingly important and it can greatly reduce the minimum ignition energy.

Fluid Dynamics

Self-Supervised Transfer Learning for Hand Mesh Recovery From Binocular Images

no code implementations ICCV 2021 Zheng Chen, Sihan Wang, Yi Sun, Xiaohong Ma

Traditional methods for RGB hand mesh recovery usually need to train a separate model for each dataset with the corresponding ground truth and are hardly adapted to new scenarios without the ground truth for supervision.

Transfer Learning

Consensus-Based Distributed Computation of Link-Based Network Metrics

no code implementations29 Dec 2020 Zheng Chen, Erik G. Larsson

Average consensus algorithms have wide applications in distributed computing systems where all the nodes agree on the average value of their initial states by only exchanging information with their local neighbors.

Distributed Computing Distributed, Parallel, and Cluster Computing Social and Information Networks Signal Processing

Time-Optimal Guidance to Intercept Moving Targets by Dubins Vehicles

no code implementations22 Dec 2020 Yuan Zheng, Xueming Shao, Zheng Chen, Wenjie Zhao

When the target's velocity is constant, by employing the geometric properties, those 4 candidates are transformed to a class of sufficiently smooth real-valued functions.

Optimization and Control

ForceReader: a BERT-based Interactive Machine Reading Comprehension Model with Attention Separation

no code implementations COLING 2020 Zheng Chen, Kangjian Wu

First, ForceReader proposes a novel solution called the Attention Separation Representation to respond to attention deconcentration.

Machine Reading Comprehension

Neural Stochastic Block Model & Scalable Community-Based Graph Learning

no code implementations16 May 2020 Zheng Chen, Xinli Yu, Yuan Ling, Xiaohua Hu

Compared with SBM, our framework is flexible, naturally allows soft labels and digestion of complex node attributes.

Community Detection Graph Attention +3

An Ontology-driven Treatment Article Retrieval System for Precision Oncology

no code implementations13 Feb 2020 Zheng Chen, Sadid A. Hasan, Joey Liu, Vivek Datla, Md Shamsuzzaman, Hafiz Khan, Mohammad S. Sorower, Gabe Mankovich, Rob van Ommering, Nevenka Dimitrova

This paper presents an ontology-driven treatment article retrieval system developed and experimented using the data and ground truths provided by the TREC 2017 precision medicine track.


Pre-Training for Query Rewriting in A Spoken Language Understanding System

no code implementations13 Feb 2020 Zheng Chen, Xing Fan, Yuan Ling, Lambert Mathias, Chenlei Guo

Then, inspired by the wide success of pre-trained contextual language embeddings, and also as a way to compensate for insufficient QR training data, we propose a language-modeling (LM) based approach to pre-train query embeddings on historical user conversation data with a voice assistant.

Entity Resolution Friction +5

Optimizing Information Freshness in a Multiple Access Channel with Heterogeneous Devices

no code implementations10 Oct 2019 Zheng Chen, Nikolaos Pappas, Emil Björnson, Erik G. Larsson

We formulate an optimization problem that aims at minimizing the average age of information (AoI) of the EH node subject to the queue stability condition of the grid-connected node.

Information Theory Networking and Internet Architecture Information Theory

Creating Navigable Space from Sparse Noisy Map Points

1 code implementation4 Mar 2019 Zheng Chen, Lantao Liu

We present a framework for creating navigable space from sparse and noisy map points generated by sparse visual SLAM methods.


Large-Scale Joint Topic, Sentiment & User Preference Analysis for Online Reviews

no code implementations14 Jan 2019 Xinli Yu, Zheng Chen, Wei-Shih Yang, Xiaohua Hu, Erjia Yan

This paper presents a non-trivial reconstruction of a previous joint topic-sentiment-preference review model TSPRA with stick-breaking representation under the framework of variational inference (VI) and stochastic variational inference (SVI).

Variational Inference

Fast Botnet Detection From Streaming Logs Using Online Lanczos Method

no code implementations19 Dec 2018 Zheng Chen, Xinli Yu, Chi Zhang, Jin Zhang, Cui Lin, Bo Song, Jianliang Gao, Xiaohua Hu, Wei-Shih Yang, Erjia Yan

Botnet, a group of coordinated bots, is becoming the main platform of malicious Internet activities like DDOS, click fraud, web scraping, spam/rumor distribution, etc.

Unifying Topic, Sentiment & Preference in an HDP-Based Rating Regression Model for Online Reviews

1 code implementation19 Dec 2018 Zheng Chen, Yong Zhang, Yue Shang, Xiaohua Hu

TSPRA combines topics (i. e. product aspects), word sentiment and user preference as regression factors, and is able to perform topic clustering, review rating prediction, sentiment analysis and what we invent as "critical aspect" analysis altogether in one framework.

Collaborative Filtering Online Review Rating +2

Correlated Anomaly Detection from Large Streaming Data

no code implementations19 Dec 2018 Zheng Chen, Xinli Yu, Yuan Ling, Bo Song, Wei Quan, Xiaohua Hu, Erjia Yan

Correlated anomaly detection (CAD) from streaming data is a type of group anomaly detection and an essential task in useful real-time data mining applications like botnet detection, financial event detection, industrial process monitor, etc.

Event Detection Group Anomaly Detection

Detecting and Explaining Causes From Text For a Time Series Event

1 code implementation EMNLP 2017 Dongyeop Kang, Varun Gangal, Ang Lu, Zheng Chen, Eduard Hovy

Our quantitative and human analysis show empirical evidence that our method successfully extracts meaningful causality relationships between time series with textual features and generates appropriate explanation between them.

Time Series Analysis

Knowledge Graph Embedding by Translating on Hyperplanes

1 code implementation AAAI 2014 2014 Zhen Wang, Jianwen Zhang, Jianlin Feng, Zheng Chen

Utilizing the one-to-many/many-to-one mapping property of a relation, we propose a simple trick to reduce the possibility of false negative labeling.

Knowledge Graph Embedding Link Prediction

Cannot find the paper you are looking for? You can Submit a new open access paper.