Search Results for author: Bo Jin

Found 33 papers, 6 papers with code

融合外部知识的开放域复述模板获取方法(An Open Domain Paraphrasing Template Acquisition Method Based on External Knowledge)

no code implementations CCL 2021 Bo Jin, Mingtong Liu, Yujie Zhang, Jinan Xu, Yufeng Chen

“如何挖掘语言资源中丰富的复述模板, 是复述研究中的一项重要任务。已有方法在人工给定种子实体对的基础上, 利用实体关系, 通过自举迭代方式, 从开放域获取复述模板, 规避对平行语料或可比语料的依赖, 但是该方法需人工给定实体对, 实体关系受限;在迭代过程中语义会发生偏移, 影响获取质量。针对这些问题, 我们考虑知识库中包含描述特定语义关系的实体对(即关系三元组), 提出融合外部知识的开放域复述模板自动获取方法。首先, 将关系三元组与开放域文本对齐, 获取关系对应文本, 并将文本中语义丰富部分泛化成变量槽, 获取关系模板;接着设计模板表示方法, 本文利用预训练语言模型, 在模板表示中融合变量槽语义;最后, 根据获得的模板表示, 设计自动聚类与筛选方法, 获取高精度的复述模板。在融合自动评测与人工评测的评价方法下, 实验结果表明, 本文提出的方法实现了在开放域数据上复述模板的自动泛化与获取, 能够获得质量高、语义一致的复述模板。”

基于枢轴语言系统融合的词汇混淆网络神经机器翻译(Neural Machine Translation for Vocabulary Confusion Network Based on Pivotal Language System Fusion)

no code implementations CCL 2021 Xiaobing Zhao, Bo Jin, Yuan Sun

“神经机器翻译在低资源语言的翻译任务中存在翻译难度大、译文质量不佳的问题。本文针对低资源语言与汉语之间没有双语平行语料的情况, 采用正反向枢轴翻译的方法, 生成了三种低资源语言到汉语的平行句对, 采用词汇级的系统融合技术, 将Transformer模型和对偶学习模型翻译生成的目标语言译文进行融合, 然后通过混淆神经网络进行词汇选择, 生成了更为优质的目标语言译文。实验证明, 本文提出的多模型融合方法在爱沙尼亚语-汉语、拉脱维亚语-汉语、罗马尼亚语-汉语这三种低资源语言翻译任务中均优于独立模型的翻译效果, 进一步提升了低资源语言神经机器翻译的译文质量。”

Machine Translation

Complementary Information Mutual Learning for Multimodality Medical Image Segmentation

no code implementations5 Jan 2024 Chuyun Shen, Wenhao Li, Haoqing Chen, Xiaoling Wang, Fengping Zhu, Yuxin Li, Xiangfeng Wang, Bo Jin

CIML adopts the idea of addition and removes inter-modal redundant information through inductive bias-driven task decomposition and message passing-based redundancy filtering.

Image Segmentation Inductive Bias +4

Can language agents be alternatives to PPO? A Preliminary Empirical Study On OpenAI Gym

1 code implementation6 Dec 2023 Junjie Sheng, Zixiao Huang, Chuyun Shen, Wenhao Li, Yun Hua, Bo Jin, Hongyuan Zha, Xiangfeng Wang

The formidable capacity for zero- or few-shot decision-making in language agents encourages us to pose a compelling question: Can language agents be alternatives to PPO agents in traditional sequential decision-making tasks?

Benchmarking Decision Making +1

LatentWarp: Consistent Diffusion Latents for Zero-Shot Video-to-Video Translation

no code implementations1 Nov 2023 Yuxiang Bao, Di Qiu, Guoliang Kang, Baochang Zhang, Bo Jin, Kaiye Wang, Pengfei Yan

As a result, the corresponding regions across the adjacent frames can share closely-related query tokens and attention outputs, which can further improve latent-level consistency to enhance visual temporal coherence of generated videos.

Denoising Optical Flow Estimation +1

EFaR 2023: Efficient Face Recognition Competition

1 code implementation8 Aug 2023 Jan Niklas Kolf, Fadi Boutros, Jurek Elliesen, Markus Theuerkauf, Naser Damer, Mohamad Alansari, Oussama Abdul Hay, Sara Alansari, Sajid Javed, Naoufel Werghi, Klemen Grm, Vitomir Štruc, Fernando Alonso-Fernandez, Kevin Hernandez Diaz, Josef Bigun, Anjith George, Christophe Ecabert, Hatef Otroshi Shahreza, Ketan Kotwal, Sébastien Marcel, Iurii Medvedev, Bo Jin, Diogo Nunes, Ahmad Hassanpour, Pankaj Khatiwada, Aafan Ahmad Toor, Bian Yang

To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size.

Lightweight Face Recognition Quantization

MediaGPT : A Large Language Model For Chinese Media

no code implementations20 Jul 2023 Zhonghao Wang, Zijia Lu, Bo Jin, Haiying Deng

Large language models (LLMs) have shown remarkable capabilities in generating high-quality text and making predictions based on large amounts of data, including the media domain.

Language Modelling Large Language Model

Negotiated Reasoning: On Provably Addressing Relative Over-Generalization

no code implementations8 Jun 2023 Junjie Sheng, Wenhao Li, Bo Jin, Hongyuan Zha, Jun Wang, Xiangfeng Wang

Recent methods have shown that assigning reasoning ability to agents can mitigate RO algorithmically and empirically, but there has been a lack of theoretical understanding of RO, let alone designing provably RO-free methods.

Multi-agent Reinforcement Learning

Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learning

no code implementations18 May 2023 Wenhao Li, Dan Qiao, Baoxiang Wang, Xiangfeng Wang, Bo Jin, Hongyuan Zha

The difficulty of appropriately assigning credit is particularly heightened in cooperative MARL with sparse reward, due to the concurrent time and structural scales involved.

Decision Making Multi-agent Reinforcement Learning +2

Boundary-aware Supervoxel-level Iteratively Refined Interactive 3D Image Segmentation with Multi-agent Reinforcement Learning

no code implementations19 Mar 2023 Chaofan Ma, Qisen Xu, Xiangfeng Wang, Bo Jin, Xiaoyun Zhang, Yanfeng Wang, Ya zhang

Interactive segmentation has recently been explored to effectively and efficiently harvest high-quality segmentation masks by iteratively incorporating user hints.

Image Segmentation Interactive Segmentation +5

Learning Roles with Emergent Social Value Orientations

no code implementations31 Jan 2023 Wenhao Li, Xiangfeng Wang, Bo Jin, Jingyi Lu, Hongyuan Zha

Social dilemmas can be considered situations where individual rationality leads to collective irrationality.

Multi-agent Reinforcement Learning Role Embedding

Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution

1 code implementation5 Jan 2023 Yan Li, Xinjiang Lu, Haoyi Xiong, Jian Tang, Jiantao Su, Bo Jin, Dejing Dou

Long-term time-series forecasting (LTTF) has become a pressing demand in many applications, such as wind power supply planning.

Decoder Time Series +2

ReAssigner: A Plug-and-Play Virtual Machine Scheduling Intensifier for Heterogeneous Requests

no code implementations29 Nov 2022 Haochuan Cui, Junjie Sheng, Bo Jin, Yiqiu Hu, Li Su, Lei Zhu, Wenli Zhou, Xiangfeng Wang

With the rapid development of cloud computing, virtual machine scheduling has become one of the most important but challenging issues for the cloud computing community, especially for practical heterogeneous request sequences.

Cloud Computing Scheduling

Learning Cooperative Oversubscription for Cloud by Chance-Constrained Multi-Agent Reinforcement Learning

no code implementations21 Nov 2022 Junjie Sheng, Lu Wang, Fangkai Yang, Bo Qiao, Hang Dong, Xiangfeng Wang, Bo Jin, Jun Wang, Si Qin, Saravan Rajmohan, QIngwei Lin, Dongmei Zhang

To address these two limitations, this paper formulates the oversubscription for cloud as a chance-constrained optimization problem and propose an effective Chance Constrained Multi-Agent Reinforcement Learning (C2MARL) method to solve this problem.

Multi-agent Reinforcement Learning reinforcement-learning +1

KnowAugNet: Multi-Source Medical Knowledge Augmented Medication Prediction Network with Multi-Level Graph Contrastive Learning

no code implementations25 Apr 2022 Yang An, Bo Jin, Xiaopeng Wei

Specifically, KnowAugNet first leverages the graph contrastive learning using graph attention network as the encoder to capture the implicit relations between homogeneous medical codes from the medical ontology graph and obtains the knowledge augmented medical codes embedding vectors.

Contrastive Learning Graph Attention +1

Obtaining Dyadic Fairness by Optimal Transport

1 code implementation9 Feb 2022 Moyi Yang, Junjie Sheng, Xiangfeng Wang, Wenyan Liu, Bo Jin, Jun Wang, Hongyuan Zha

Fairness has been taken as a critical metric in machine learning models, which is considered as an important component of trustworthy machine learning.

Fairness Link Prediction

Multi-Agent Path Finding with Prioritized Communication Learning

1 code implementation8 Feb 2022 Wenhao Li, Hongjun Chen, Bo Jin, Wenzhe Tan, Hongyuan Zha, Xiangfeng Wang

The learning-based, fully decentralized framework has been introduced to alleviate real-time problems and simultaneously pursue optimal planning policy.

Multi-Agent Path Finding Multi-agent Reinforcement Learning +1

ViR:the Vision Reservoir

no code implementations27 Dec 2021 Xian Wei, Bin Wang, Mingsong Chen, Ji Yuan, Hai Lan, Jiehuang Shi, Xuan Tang, Bo Jin, Guozhang Chen, Dongping Yang

To address these problems, a novel method, namely, Vision Reservoir computing (ViR), is proposed here for image classification, as a parallel to ViT.

Classification Image Classification

VMAgent: Scheduling Simulator for Reinforcement Learning

2 code implementations9 Dec 2021 Junjie Sheng, Shengliang Cai, Haochuan Cui, Wenhao Li, Yun Hua, Bo Jin, Wenli Zhou, Yiqiu Hu, Lei Zhu, Qian Peng, Hongyuan Zha, Xiangfeng Wang

A novel simulator called VMAgent is introduced to help RL researchers better explore new methods, especially for virtual machine scheduling.

Cloud Computing reinforcement-learning +2

Interactive Medical Image Segmentation with Self-Adaptive Confidence Calibration

no code implementations15 Nov 2021 Wenhao Li, Qisen Xu, Chuyun Shen, Bin Hu, Fengping Zhu, Yuxin Li, Bo Jin, Xiangfeng Wang

Based on the confidential information, a self-adaptive reward function is designed to provide more detailed feedback, and a simulated label generation mechanism is proposed on unsupervised data to reduce over-reliance on labeled data.

Image Segmentation Interactive Segmentation +4

MeSIN: Multilevel Selective and Interactive Network for Medication Recommendation

no code implementations22 Apr 2021 Yang An, Liang Zhang, Mao You, Xueqing Tian, Bo Jin, Xiaopeng Wei

Second, we incorporate a novel interactive long-short term memory network (InLSTM) to reinforce the interactions of multilevel medical sequences in EHR data with the help of the calibrated memory-augmented cell and an enhanced input gate.

Decision Making

Dealing with Non-Stationarity in MARL via Trust-Region Decomposition

no code implementations ICLR 2022 Wenhao Li, Xiangfeng Wang, Bo Jin, Junjie Sheng, Hongyuan Zha

In this paper, we introduce a novel notion, the $\delta$-measurement, to explicitly measure the non-stationarity of a policy sequence, which can be further proved to be bounded by the KL-divergence of consecutive joint policies.

Multi-agent Reinforcement Learning

Structured Diversification Emergence via Reinforced Organization Control and Hierarchical Consensus Learning

no code implementations9 Feb 2021 Wenhao Li, Xiangfeng Wang, Bo Jin, Junjie Sheng, Yun Hua, Hongyuan Zha

In order to improve the efficiency of cooperation and exploration, we propose a structured diversification emergence MARL framework named {\sc{Rochico}} based on reinforced organization control and hierarchical consensus learning.

Multi-agent Reinforcement Learning

Fair Differential Privacy Can Mitigate the Disparate Impact on Model Accuracy

no code implementations1 Jan 2021 Wenyan Liu, Xiangfeng Wang, Xingjian Lu, Junhong Cheng, Bo Jin, Xiaoling Wang, Hongyuan Zha

This paper proposes a fair differential privacy algorithm (FairDP) to mitigate the disparate impact on model accuracy for each class.


F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning

no code implementations17 Apr 2020 Wenhao Li, Bo Jin, Xiangfeng Wang, Junchi Yan, Hongyuan Zha

Traditional centralized multi-agent reinforcement learning (MARL) algorithms are sometimes unpractical in complicated applications, due to non-interactivity between agents, curse of dimensionality and computation complexity.

Multi-agent Reinforcement Learning Reinforcement Learning (RL) +2

HMRL: Hyper-Meta Learning for Sparse Reward Reinforcement Learning Problem

no code implementations11 Feb 2020 Yun Hua, Xiangfeng Wang, Bo Jin, Wenhao Li, Junchi Yan, Xiaofeng He, Hongyuan Zha

In spite of the success of existing meta reinforcement learning methods, they still have difficulty in learning a meta policy effectively for RL problems with sparse reward.

Meta-Learning Meta Reinforcement Learning +2

Iteratively-Refined Interactive 3D Medical Image Segmentation with Multi-Agent Reinforcement Learning

no code implementations CVPR 2020 Xuan Liao, Wenhao Li, Qisen Xu, Xiangfeng Wang, Bo Jin, Xiaoyun Zhang, Ya zhang, Yan-Feng Wang

We here propose to model the dynamic process of iterative interactive image segmentation as a Markov decision process (MDP) and solve it with reinforcement learning (RL).

Image Segmentation Medical Image Segmentation +5

Heterogeneous Graph-based Knowledge Transfer for Generalized Zero-shot Learning

no code implementations20 Nov 2019 Jun-Jie Wang, Xiangfeng Wang, Bo Jin, Junchi Yan, Wenjie Zhang, Hongyuan Zha

To this end, we propose a novel heterogeneous graph-based knowledge transfer method (HGKT) for GZSL, agnostic to unseen classes and instances, by leveraging graph neural network.

Generalized Zero-Shot Learning Transfer Learning

Multi-view Reconstructive Preserving Embedding for Dimension Reduction

no code implementations25 Jul 2018 Huibing Wang, Lin Feng, Adong Kong, Bo Jin

With the development of feature extraction technique, one sample always can be represented by multiple features which locate in high-dimensional space.

Dimensionality Reduction Document Classification +3

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