Search Results for author: Yu Zhao

Found 95 papers, 32 papers with code

Alibaba’s Submission for the WMT 2020 APE Shared Task: Improving Automatic Post-Editing with Pre-trained Conditional Cross-Lingual BERT

no code implementations WMT (EMNLP) 2020 Jiayi Wang, Ke Wang, Kai Fan, Yuqi Zhang, Jun Lu, Xin Ge, Yangbin Shi, Yu Zhao

We also apply an imitation learning strategy to augment a reasonable amount of pseudo APE training data, potentially preventing the model to overfit on the limited real training data and boosting the performance on held-out data.

Automatic Post-Editing Benchmarking +5

Graph Dimension Attention Networks for Enterprise Credit Assessment

no code implementations16 Jul 2024 Shaopeng Wei, Beni Egressy, Xingyan Chen, Yu Zhao, Fuzhen Zhuang, Roger Wattenhofer, Gang Kou

Enterprise credit assessment is critical for evaluating financial risk, and Graph Neural Networks (GNNs), with their advanced capability to model inter-entity relationships, are a natural tool to get a deeper understanding of these financial networks.

Retrieval-style In-Context Learning for Few-shot Hierarchical Text Classification

1 code implementation25 Jun 2024 Huiyao Chen, Yu Zhao, Zulong Chen, Mengjia Wang, Liangyue Li, Meishan Zhang, Min Zhang

Hierarchical text classification (HTC) is an important task with broad applications, while few-shot HTC has gained increasing interest recently.

Contrastive Learning few-shot-htc +7

A Survey of Retrieval Algorithms in Ad and Content Recommendation Systems

no code implementations21 Jun 2024 Yu Zhao, Fang Liu

This survey examines the most effective retrieval algorithms utilized in ad recommendation and content recommendation systems.

Recommendation Systems Retrieval

TemPrompt: Multi-Task Prompt Learning for Temporal Relation Extraction in RAG-based Crowdsourcing Systems

no code implementations21 Jun 2024 Jing Yang, Yu Zhao, Linyao Yang, Xiao Wang, Long Chen, Fei-Yue Wang

Temporal relation extraction (TRE) aims to grasp the evolution of events or actions, and thus shape the workflow of associated tasks, so it holds promise in helping understand task requests initiated by requesters in crowdsourcing systems.

Contrastive Learning Language Modelling +4

A Simple and Effective $L_2$ Norm-Based Strategy for KV Cache Compression

no code implementations17 Jun 2024 Alessio Devoto, Yu Zhao, Simone Scardapane, Pasquale Minervini

The deployment of large language models (LLMs) is often hindered by the extensive memory requirements of the Key-Value (KV) cache, especially as context lengths increase.

Decoder Language Modelling

SciKnowEval: Evaluating Multi-level Scientific Knowledge of Large Language Models

1 code implementation13 Jun 2024 Kehua Feng, Keyan Ding, Weijie Wang, Xiang Zhuang, Zeyuan Wang, Ming Qin, Yu Zhao, Jianhua Yao, Qiang Zhang, Huajun Chen

The burgeoning utilization of Large Language Models (LLMs) in scientific research necessitates advanced benchmarks capable of evaluating their understanding and application of scientific knowledge comprehensively.


RAG-based Crowdsourcing Task Decomposition via Masked Contrastive Learning with Prompts

no code implementations4 Jun 2024 Jing Yang, Xiao Wang, Yu Zhao, Yuhang Liu, Fei-Yue Wang

Therefore, we present a Prompt-Based Contrastive learning framework for TD (PBCT), which incorporates a prompt-based trigger detector to overcome dependence.

Common Sense Reasoning Contrastive Learning +3

A Comprehensive Survey on Underwater Image Enhancement Based on Deep Learning

no code implementations30 May 2024 Xiaofeng Cong, Yu Zhao, Jie Gui, JunMing Hou, DaCheng Tao

Underwater image enhancement (UIE) presents a significant challenge within computer vision research.

Disentanglement UIE

Utilizing Large Language Models for Information Extraction from Real Estate Transactions

no code implementations28 Apr 2024 Yu Zhao, Haoxiang Gao

Real estate sales contracts contain crucial information for property transactions, but manual extraction of data can be time-consuming and error-prone.

Tele-FLM Technical Report

no code implementations25 Apr 2024 Xiang Li, Yiqun Yao, Xin Jiang, Xuezhi Fang, Chao Wang, Xinzhang Liu, Zihan Wang, Yu Zhao, Xin Wang, Yuyao Huang, Shuangyong Song, Yongxiang Li, Zheng Zhang, Bo Zhao, Aixin Sun, Yequan Wang, Zhongjiang He, Zhongyuan Wang, Xuelong Li, Tiejun Huang

Large language models (LLMs) have showcased profound capabilities in language understanding and generation, facilitating a wide array of applications.

Language Modelling Large Language Model

Tensor-based Graph Learning with Consistency and Specificity for Multi-view Clustering

1 code implementation27 Mar 2024 Long Shi, Lei Cao, Yunshan Ye, Yu Zhao, Badong Chen

In the context of multi-view clustering, graph learning is recognized as a crucial technique, which generally involves constructing an adaptive neighbor graph based on probabilistic neighbors, and then learning a consensus graph to for clustering.

Clustering Graph Learning +1

Multi-Scale Subgraph Contrastive Learning

no code implementations5 Mar 2024 Yanbei Liu, Yu Zhao, Xiao Wang, Lei Geng, Zhitao Xiao

By an experimental analysis, we discover the semantic information of an augmented graph structure may be not consistent as original graph structure, and whether two augmented graphs are positive or negative pairs is highly related with the multi-scale structures.

Contrastive Learning Graph Classification

Towards Optimal Customized Architecture for Heterogeneous Federated Learning with Contrastive Cloud-Edge Model Decoupling

1 code implementation4 Mar 2024 Xingyan Chen, Tian Du, Mu Wang, Tiancheng Gu, Yu Zhao, Gang Kou, Changqiao Xu, Dapeng Oliver Wu

To address these issues, we propose a novel federated learning framework called FedCMD, a model decoupling tailored to the Cloud-edge supported federated learning that separates deep neural networks into a body for capturing shared representations in Cloud and a personalized head for migrating data heterogeneity.

Federated Learning

Analysing The Impact of Sequence Composition on Language Model Pre-Training

1 code implementation21 Feb 2024 Yu Zhao, Yuanbin Qu, Konrad Staniszewski, Szymon Tworkowski, Wei Liu, Piotr Miłoś, Yuxiang Wu, Pasquale Minervini

In this work, we find that applying causal masking can lead to the inclusion of distracting information from previous documents during pre-training, which negatively impacts the performance of the models on language modelling and downstream tasks.

In-Context Learning Language Modelling +1

StableMask: Refining Causal Masking in Decoder-only Transformer

no code implementations7 Feb 2024 Qingyu Yin, Xuzheng He, Xiang Zhuang, Yu Zhao, Jianhua Yao, Xiaoyu Shen, Qiang Zhang

The decoder-only Transformer architecture with causal masking and relative position encoding (RPE) has become the de facto choice in language modeling.

Decoder Language Modelling +1

Nonlinear subspace clustering by functional link neural networks

no code implementations3 Feb 2024 Long Shi, Lei Cao, Zhongpu Chen, Badong Chen, Yu Zhao

Additionally, we introduce a convex combination subspace clustering scheme, which combining a linear subspace clustering method with the functional link neural network subspace clustering approach.

Clustering Computational Efficiency

Next-Generation Simulation Illuminates Scientific Problems of Organised Complexity

no code implementations18 Jan 2024 Cheng Wang, Chuwen Wang, Wang Zhang, Shirong Zeng, Yu Zhao, Ronghui Ning, Changjun Jiang

SBS represents a higher level of paradigms integration based on foundational models to simulate complex systems, such as social systems involving sophisticated human strategies and behaviours.

Weather Forecasting

Structured Packing in LLM Training Improves Long Context Utilization

no code implementations28 Dec 2023 Konrad Staniszewski, Szymon Tworkowski, Sebastian Jaszczur, Yu Zhao, Henryk Michalewski, Łukasz Kuciński, Piotr Miłoś

Recent advancements in long-context large language models have attracted significant attention, yet their practical applications often suffer from suboptimal context utilization.

Information Retrieval Retrieval

Improving Neural Machine Translation by Multi-Knowledge Integration with Prompting

no code implementations8 Dec 2023 Ke Wang, Jun Xie, Yuqi Zhang, Yu Zhao

In this work, we focus on how to integrate multi-knowledge, multiple types of knowledge, into NMT models to enhance the performance with prompting.

Decoder Machine Translation +2

Generating Progressive Images from Pathological Transitions via Diffusion Model

2 code implementations21 Nov 2023 Zeyu Liu, Tianyi Zhang, Yufang He, Yunlu Feng, Yu Zhao, Guanglei Zhang

Deep learning is widely applied in computer-aided pathological diagnosis, which alleviates the pathologist workload and provide timely clinical analysis.

Data Augmentation Diversity +1

CPIA Dataset: A Comprehensive Pathological Image Analysis Dataset for Self-supervised Learning Pre-training

1 code implementation27 Oct 2023 Nan Ying, Yanli Lei, Tianyi Zhang, Shangqing Lyu, Chunhui Li, Sicheng Chen, Zeyu Liu, Yu Zhao, Guanglei Zhang

This paper presents the comprehensive pathological image analysis (CPIA) dataset, a large-scale SSL pre-training dataset combining 103 open-source datasets with extensive standardization.

Self-Supervised Learning Transfer Learning +1

Taming Gradient Variance in Federated Learning with Networked Control Variates

no code implementations26 Oct 2023 Xingyan Chen, Yaling Liu, Huaming Du, Mu Wang, Yu Zhao

To address this, we introduce a novel Networked Control Variates (FedNCV) framework for Federated Learning.

Federated Learning

Synslator: An Interactive Machine Translation Tool with Online Learning

no code implementations8 Oct 2023 Jiayi Wang, Ke Wang, Fengming Zhou, Chengyu Wang, Zhiyong Fu, Zeyu Feng, Yu Zhao, Yuqi Zhang

Interactive machine translation (IMT) has emerged as a progression of the computer-aided translation paradigm, where the machine translation system and the human translator collaborate to produce high-quality translations.

Language Modelling Machine Translation +1

Constructing Holistic Spatio-Temporal Scene Graph for Video Semantic Role Labeling

no code implementations9 Aug 2023 Yu Zhao, Hao Fei, Yixin Cao, Bobo Li, Meishan Zhang, Jianguo Wei, Min Zhang, Tat-Seng Chua

A scene-event mapping mechanism is first designed to bridge the gap between the underlying scene structure and the high-level event semantic structure, resulting in an overall hierarchical scene-event (termed ICE) graph structure.

Semantic Role Labeling

Revisiting Disentanglement and Fusion on Modality and Context in Conversational Multimodal Emotion Recognition

no code implementations8 Aug 2023 Bobo Li, Hao Fei, Lizi Liao, Yu Zhao, Chong Teng, Tat-Seng Chua, Donghong Ji, Fei Li

On the other hand, during the feature fusion stage, we propose a Contribution-aware Fusion Mechanism (CFM) and a Context Refusion Mechanism (CRM) for multimodal and context integration, respectively.

Contrastive Learning Disentanglement +2

A Noisy-Label-Learning Formulation for Immune Repertoire Classification and Disease-Associated Immune Receptor Sequence Identification

1 code implementation29 Jul 2023 Mingcai Chen, Yu Zhao, Zhonghuang Wang, Bing He, Jianhua Yao

Immune repertoire classification, a typical multiple instance learning (MIL) problem, is a frontier research topic in computational biology that makes transformative contributions to new vaccines and immune therapies.

Classification Immune Repertoire Classification +1

DNAGPT: A Generalized Pre-trained Tool for Versatile DNA Sequence Analysis Tasks

no code implementations11 Jul 2023 Daoan Zhang, Weitong Zhang, Yu Zhao, JianGuo Zhang, Bing He, Chenchen Qin, Jianhua Yao

Pre-trained large language models demonstrate potential in extracting information from DNA sequences, yet adapting to a variety of tasks and data modalities remains a challenge.

Binary Classification DNA analysis +1

Mining Interest Trends and Adaptively Assigning SampleWeight for Session-based Recommendation

no code implementations20 Jun 2023 Kai Ouyang, Xianghong Xu, Miaoxin Chen, Zuotong Xie, Hai-Tao Zheng, Shuangyong Song, Yu Zhao

Session-based Recommendation (SR) aims to predict users' next click based on their behavior within a short period, which is crucial for online platforms.

Session-Based Recommendations

Non-parametric, Nearest-neighbor-assisted Fine-tuning for Neural Machine Translation

no code implementations23 May 2023 Jiayi Wang, Ke Wang, Yuqi Zhang, Yu Zhao, Pontus Stenetorp

We explore whether such non-parametric models can improve machine translation models at the fine-tuning stage by incorporating statistics from the kNN predictions to inform the gradient updates for a baseline translation model.

Machine Translation Translation

Generating Visual Spatial Description via Holistic 3D Scene Understanding

1 code implementation19 May 2023 Yu Zhao, Hao Fei, Wei Ji, Jianguo Wei, Meishan Zhang, Min Zhang, Tat-Seng Chua

With an external 3D scene extractor, we obtain the 3D objects and scene features for input images, based on which we construct a target object-centered 3D spatial scene graph (Go3D-S2G), such that we model the spatial semantics of target objects within the holistic 3D scenes.

Scene Understanding Text Generation

From Alignment to Entailment: A Unified Textual Entailment Framework for Entity Alignment

1 code implementation19 May 2023 Yu Zhao, Yike Wu, Xiangrui Cai, Ying Zhang, Haiwei Zhang, Xiaojie Yuan

Our approach captures the unified correlation pattern of two kinds of information between entities, and explicitly models the fine-grained interaction between original entity information.

Attribute Entity Alignment +3

Diffusion Models in NLP: A Survey

no code implementations14 Mar 2023 Yuansong Zhu, Yu Zhao

Diffusion models have become a powerful family of deep generative models, with record-breaking performance in many applications.

Image Generation Text Generation

Causal conditional hidden Markov model for multimodal traffic prediction

1 code implementation19 Jan 2023 Yu Zhao, Pan Deng, Junting Liu, Xiaofeng Jia, Mulan Wang

Recent works overemphasize spatio-temporal correlations of traffic flow, ignoring the physical concepts that lead to the generation of observations and their causal relationship.

Management Traffic Prediction

Spatio-temporal neural structural causal models for bike flow prediction

1 code implementation19 Jan 2023 Pan Deng, Yu Zhao, Junting Liu, Xiaofeng Jia, Mulan Wang

In addition, due to the disturbance of incomplete observations in the data, random contextual conditions lead to spurious correlations between data and features, making the prediction of the model ineffective in special scenarios.

counterfactual Traffic Prediction

Graph Learning and Its Advancements on Large Language Models: A Holistic Survey

no code implementations17 Dec 2022 Shaopeng Wei, Yu Zhao, Xingyan Chen, Qing Li, Fuzhen Zhuang, Ji Liu, Fuji Ren, Gang Kou

Different from previous surveys on graph learning, we provide a holistic review that analyzes current works from the perspective of graph structure, and discusses the latest applications, trends, and challenges in graph learning.

Graph Learning Representation Learning

A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data Perspective

no code implementations28 Nov 2022 Yu Zhao, Huaming Du, Qing Li, Fuzhen Zhuang, Ji Liu, Gang Kou

In contrast, this paper attempts to provide a systematic literature survey of enterprise risk analysis approaches from Big Data perspective, which reviews more than 250 representative articles in the past almost 50 years (from 1968 to 2023).


ESIE-BERT: Enriching Sub-words Information Explicitly with BERT for Joint Intent Classification and SlotFilling

no code implementations27 Nov 2022 Yu Guo, Zhilong Xie, Xingyan Chen, Huangen Chen, Leilei Wang, Huaming Du, Shaopeng Wei, Yu Zhao, Qing Li, Gang Wu

We address the problem by introducing a novel joint method on top of BERT which explicitly models the multiple sub-tokens features after wordpiece tokenization, thereby contributing to the two tasks.

intent-classification Intent Classification +5

TSMind: Alibaba and Soochow University's Submission to the WMT22 Translation Suggestion Task

no code implementations16 Nov 2022 Xin Ge, Ke Wang, Jiayi Wang, Nini Xiao, Xiangyu Duan, Yu Zhao, Yuqi Zhang

The leader board finally shows that our submissions are ranked first in three of four language directions in the Naive TS task of the WMT22 Translation Suggestion task.

Data Augmentation Language Modelling +1

Learning with Noisy Labels over Imbalanced Subpopulations

no code implementations16 Nov 2022 Mingcai Chen, Yu Zhao, Bing He, Zongbo Han, Bingzhe Wu, Jianhua Yao

Then, we refurbish the noisy labels using the estimated clean probabilities and the pseudo-labels from the model's predictions.

Learning with noisy labels

Easy Guided Decoding in Providing Suggestions for Interactive Machine Translation

1 code implementation14 Nov 2022 Ke Wang, Xin Ge, Jiayi Wang, Yu Zhao, Yuqi Zhang

Human translators perform post editing on machine translations to correct errors in the scene of computer aided translation.

Machine Translation NMT +1

An Efficient Memory-Augmented Transformer for Knowledge-Intensive NLP Tasks

1 code implementation30 Oct 2022 Yuxiang Wu, Yu Zhao, Baotian Hu, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel

Experiments on various knowledge-intensive tasks such as question answering and dialogue datasets show that, simply augmenting parametric models (T5-base) using our method produces more accurate results (e. g., 25. 8 -> 44. 3 EM on NQ) while retaining a high throughput (e. g., 1000 queries/s on NQ).

Computational Efficiency Question Answering +1

Visual Spatial Description: Controlled Spatial-Oriented Image-to-Text Generation

1 code implementation20 Oct 2022 Yu Zhao, Jianguo Wei, Zhichao Lin, Yueheng Sun, Meishan Zhang, Min Zhang

Accordingly, we manually annotate a dataset to facilitate the investigation of the newly-introduced task and build several benchmark encoder-decoder models by using VL-BART and VL-T5 as backbones.

Decoder Image Captioning +1

MoSE: Modality Split and Ensemble for Multimodal Knowledge Graph Completion

1 code implementation17 Oct 2022 Yu Zhao, Xiangrui Cai, Yike Wu, Haiwei Zhang, Ying Zhang, Guoqing Zhao, Ning Jiang

Based on these embeddings, in the inference phase, we first make modality-split predictions and then exploit various ensemble methods to combine the predictions with different weights, which models the modality importance dynamically.

Knowledge Graph Completion Relation +1

Overcoming Language Priors in Visual Question Answering via Distinguishing Superficially Similar Instances

1 code implementation COLING 2022 Yike Wu, Yu Zhao, Shiwan Zhao, Ying Zhang, Xiaojie Yuan, Guoqing Zhao, Ning Jiang

In this work, we define the training instances with the same question type but different answers as \textit{superficially similar instances}, and attribute the language priors to the confusion of VQA model on such instances.

Attribute Question Answering +1

Shuffle Instances-based Vision Transformer for Pancreatic Cancer ROSE Image Classification

1 code implementation14 Aug 2022 Tianyi Zhang, Youdan Feng, Yunlu Feng, Yu Zhao, Yanli Lei, Nan Ying, Zhiling Yan, Yufang He, Guanglei Zhang

The rapid on-site evaluation (ROSE) technique can signifi-cantly accelerate the diagnosis of pancreatic cancer by im-mediately analyzing the fast-stained cytopathological images.

Image Classification

Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning

1 code implementation2 Aug 2022 Qiong Wu, Yu Zhao, Qiang Fan, Pingyi Fan, Jiangzhou Wang, Cui Zhang

In addition, we consider the mobility of vehicles and propose a deep reinforcement learning algorithm to obtain the optimal cooperative caching location for the predicted popular contents in order to optimize the content transmission delay.

Edge-computing Federated Learning +2

Time-Dependent Performance Modeling for Platooning Communications at Intersection

no code implementations2 Aug 2022 Qiong Wu, Yu Zhao, Qiang Fan

In this paper, we construct the time-dependent model to evaluate the platooning communication performance at the intersection based on the initial movement characteristics.

Autonomous Driving

MSDF: A General Open-Domain Multi-Skill Dialog Framework

no code implementations17 Jun 2022 Yu Zhao, Xinshuo Hu, Yunxin Li, Baotian Hu, Dongfang Li, Sichao Chen, Xiaolong Wang

In this paper, we propose a general Multi-Skill Dialog Framework, namely MSDF, which can be applied in different dialog tasks (e. g. knowledge grounded dialog and persona based dialog).


Medical Dialogue Response Generation with Pivotal Information Recalling

no code implementations17 Jun 2022 Yu Zhao, Yunxin Li, Yuxiang Wu, Baotian Hu, Qingcai Chen, Xiaolong Wang, Yuxin Ding, Min Zhang

To mitigate this problem, we propose a medical response generation model with Pivotal Information Recalling (MedPIR), which is built on two components, i. e., knowledge-aware dialogue graph encoder and recall-enhanced generator.

Dialogue Generation Graph Attention +1

Combining Intra-Risk and Contagion Risk for Enterprise Bankruptcy Prediction Using Graph Neural Networks

1 code implementation1 Feb 2022 Yu Zhao, Shaopeng Wei, Yu Guo, Qing Yang, Xingyan Chen, Qing Li, Fuzhen Zhuang, Ji Liu, Gang Kou

This study for the first time considers both types of risk and their joint effects in bankruptcy prediction.

Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks

1 code implementation11 Jan 2022 Yu Zhao, Huaming Du, Ying Liu, Shaopeng Wei, Xingyan Chen, Fuzhen Zhuang, Qing Li, Ji Liu, Gang Kou

Stock Movement Prediction (SMP) aims at predicting listed companies' stock future price trend, which is a challenging task due to the volatile nature of financial markets.

Implicit Relations Stock Prediction

QEMind: Alibaba's Submission to the WMT21 Quality Estimation Shared Task

no code implementations30 Dec 2021 Jiayi Wang, Ke Wang, Boxing Chen, Yu Zhao, Weihua Luo, Yuqi Zhang

Quality Estimation, as a crucial step of quality control for machine translation, has been explored for years.

Machine Translation Sentence +1

Learning Bi-typed Multi-relational Heterogeneous Graph via Dual Hierarchical Attention Networks

1 code implementation24 Dec 2021 Yu Zhao, Shaopeng Wei, Huaming Du, Xingyan Chen, Qing Li, Fuzhen Zhuang, Ji Liu, Gang Kou

To address this issue, we propose a novel Dual Hierarchical Attention Networks (DHAN) based on the bi-typed multi-relational heterogeneous graphs to learn comprehensive node representations with the intra-class and inter-class attention-based encoder under a hierarchical mechanism.

Graph Learning

Fast Electromagnetic Validations of Large-Scale Digital Coding Metasurfaces Accelerated by Recurrence Rebuild and Retrieval Method

no code implementations4 Dec 2021 Yu Zhao, Shang Xiang, Long Li

The recurrence rebuild and retrieval method (R3M) is proposed in this paper to accelerate the electromagnetic (EM) validations of large-scale digital coding metasurfaces (DCMs).


Deep Keyphrase Completion

no code implementations29 Oct 2021 Yu Zhao, Jia Song, Huali Feng, Fuzhen Zhuang, Qing Li, Xiaojie Wang, Ji Liu

Keyphrase provides accurate information of document content that is highly compact, concise, full of meanings, and widely used for discourse comprehension, organization, and text retrieval.

Decoder Keyphrase Extraction +2

GlyphCRM: Bidirectional Encoder Representation for Chinese Character with its Glyph

no code implementations1 Jul 2021 Yunxin Li, Yu Zhao, Baotian Hu, Qingcai Chen, Yang Xiang, Xiaolong Wang, Yuxin Ding, Lin Ma

Previous works indicate that the glyph of Chinese characters contains rich semantic information and has the potential to enhance the representation of Chinese characters.

Differentiable Channel Sparsity Search via Weight Sharing within Filters

no code implementations28 Oct 2020 Yu Zhao, Chung-Kuei Lee

Experimental results of semantic segmentation and image super resolution indicate that task-specific search achieves better performance than transferring slim models, demonstrating the wide applicability and high efficiency of DCSS.

General Classification Image Classification +3

Connecting Embeddings for Knowledge Graph Entity Typing

1 code implementation ACL 2020 Yu Zhao, Anxiang Zhang, Ruobing Xie, Kang Liu, Xiaojie Wang

In this paper, we propose a novel approach for KG entity typing which is trained by jointly utilizing local typing knowledge from existing entity type assertions and global triple knowledge from KGs.

Entity Typing Knowledge Graph Completion +1

Feedback Graph Attention Convolutional Network for Medical Image Enhancement

no code implementations24 Jun 2020 Xiaobin Hu, Yanyang Yan, Wenqi Ren, Hongwei Li, Yu Zhao, Amirhossein Bayat, Bjoern Menze

To well exploit global structural information and texture details, we propose a novel biomedical image enhancement network, named Feedback Graph Attention Convolutional Network (FB-GACN).

Graph Attention Graph Similarity +3

Graph-augmented Convolutional Networks on Drug-Drug Interactions Prediction

no code implementations8 Dec 2019 Yi Zhong, Xueyu Chen, Yu Zhao, Xiaoming Chen, Tingfang Gao, Zuquan Weng

We propose an end-to-end model to predict drug-drug interactions (DDIs) by employing graph-augmented convolutional networks.

Drug Discovery

SmartBullets: A Cloud-Assisted Bullet Screen Filter based on Deep Learning

1 code implementation15 May 2019 Haoran Niu, Jiangnan Li, Yu Zhao

Although the bullet-screen video websites have provided filter functions based on regular expression, bad bullets can still easily pass the filter through making a small modification.

ADMM-IDNN: Iteratively Double-reweighted Nuclear Norm Algorithm for Group-prior based Nonconvex Compressed Sensing via ADMM

no code implementations23 Mar 2019 Yunyi Li, Fei Dai, Yu Zhao, Xiefeng Cheng, Guan Gui

Group-prior based regularization method has led to great successes in various image processing tasks, which can usually be considered as a low-rank matrix minimization problem.

Image and Video Processing

SCEF: A Support-Confidence-aware Embedding Framework for Knowledge Graph Refinement

no code implementations18 Feb 2019 Yu Zhao, Ji Liu

Knowledge graph (KG) refinement mainly aims at KG completion and correction (i. e., error detection).


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

Hierarchical multi-class segmentation of glioma images using networks with multi-level activation function

no code implementations22 Oct 2018 Xiaobin Hu, Hongwei Li, Yu Zhao, Chao Dong, Bjoern H. Menze, Marie Piraud

Based on the same start-of-the-art network architecture, the accuracy of nested-class (enhancing tumor) is reasonably improved from 69% to 72% compared with the traditional Softmax-based method which blind to topological prior.

Brain Tumor Segmentation Tumor Segmentation

A novel active learning framework for classification: using weighted rank aggregation to achieve multiple query criteria

no code implementations27 Sep 2018 Yu Zhao, Zhenhui Shi, Jingyang Zhang, Dong Chen, Lixu Gu

The proposed method serves as a heuristic means to select high-value samples of high scalability and generality and is implemented through a three-step process: (1) the transformation of the sample selection to sample ranking and scoring, (2) the computation of the self-adaptive weights of each criterion, and (3) the weighted aggregation of each sample rank list.

Active Learning General Classification

Modeling 4D fMRI Data via Spatio-Temporal Convolutional Neural Networks (ST-CNN)

no code implementations31 May 2018 Yu Zhao, Xiang Li, Wei zhang, Shijie Zhao, Milad Makkie, Mo Zhang, Quanzheng Li, Tianming Liu

Simultaneous modeling of the spatio-temporal variation patterns of brain functional network from 4D fMRI data has been an important yet challenging problem for the field of cognitive neuroscience and medical image analysis.

Brain Decoding Network Identification

A Robust AUC Maximization Framework with Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification

no code implementations18 Mar 2018 Ke Ren, Haichuan Yang, Yu Zhao, Mingshan Xue, Hongyu Miao, Shuai Huang, Ji Liu

The positive-unlabeled (PU) classification is a common scenario in real-world applications such as healthcare, text classification, and bioinformatics, in which we only observe a few samples labeled as "positive" together with a large volume of "unlabeled" samples that may contain both positive and negative samples.

EEG feature selection +5

Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors

4 code implementations22 Aug 2017 Yu Zhao, Rennong Yang, Guillaume Chevalier, Maoguo Gong

Human activity recognition (HAR) has become a popular topic in research because of its wide application.

Human Activity Recognition

Applying Deep Bidirectional LSTM and Mixture Density Network for Basketball Trajectory Prediction

no code implementations19 Aug 2017 Yu Zhao, Rennong Yang, Guillaume Chevalier, Rajiv Shah, Rob Romijnders

In the hit-or-miss classification experiment, the proposed model outperformed other models in terms of the convergence speed and accuracy.

Time Series Time Series Analysis +1

Neural Headline Generation with Sentence-wise Optimization

no code implementations7 Apr 2016 Ayana, Shiqi Shen, Yu Zhao, Zhiyuan Liu, Maosong Sun

Recently, neural models have been proposed for headline generation by learning to map documents to headlines with recurrent neural networks.

Headline Generation Sentence

Intrinsically Motivated Learning of Visual Motion Perception and Smooth Pursuit

no code implementations14 Feb 2014 Chong Zhang, Yu Zhao, Jochen Triesch, Bertram E. Shi

We extend the framework of efficient coding, which has been used to model the development of sensory processing in isolation, to model the development of the perception/action cycle.

Reinforcement Learning (RL)

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