Search Results for author: Mingyang Chen

Found 22 papers, 16 papers with code

Lion: Adversarial Distillation of Closed-Source Large Language Model

1 code implementation22 May 2023 Yuxin Jiang, Chunkit Chan, Mingyang Chen, Wei Wang

The practice of transferring knowledge from a sophisticated, closed-source large language model (LLM) to a compact, open-source LLM has garnered considerable attention.

Instruction Following Knowledge Distillation +2

NeuralKG-ind: A Python Library for Inductive Knowledge Graph Representation Learning

1 code implementation28 Apr 2023 Wen Zhang, Zhen Yao, Mingyang Chen, Zhiwei Huang, Huajun Chen

Since the dynamic characteristics of knowledge graphs, many inductive knowledge graph representation learning (KGRL) works have been proposed in recent years, focusing on enabling prediction over new entities.

Graph Representation Learning Knowledge Graphs

Modality-Aware Negative Sampling for Multi-modal Knowledge Graph Embedding

1 code implementation23 Apr 2023 Yichi Zhang, Mingyang Chen, Wen Zhang

Negative sampling (NS) is widely used in knowledge graph embedding (KGE), which aims to generate negative triples to make a positive-negative contrast during training.

Knowledge Graph Embedding Multi-modal Knowledge Graph

Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer

1 code implementation3 Mar 2023 Wen Zhang, Yushan Zhu, Mingyang Chen, Yuxia Geng, Yufeng Huang, Yajing Xu, Wenting Song, Huajun Chen

Through experiments, we justify that the pretrained KGTransformer could be used off the shelf as a general and effective KRF module across KG-related tasks.

Image Classification Knowledge Graphs +3

Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embedding

1 code implementation3 Feb 2023 Mingyang Chen, Wen Zhang, Zhen Yao, Yushan Zhu, Yang Gao, Jeff Z. Pan, Huajun Chen

In our proposed model, Entity-Agnostic Representation Learning (EARL), we only learn the embeddings for a small set of entities and refer to them as reserved entities.

Entity Embeddings Knowledge Graph Embedding +2

Analogical Inference Enhanced Knowledge Graph Embedding

1 code implementation3 Jan 2023 Zhen Yao, Wen Zhang, Mingyang Chen, Yufeng Huang, Yi Yang, Huajun Chen

And in AnKGE, we train an analogy function for each level of analogical inference with the original element embedding from a well-trained KGE model as input, which outputs the analogical object embedding.

Knowledge Graph Embedding Knowledge Graphs +1

Boosting Accuracy and Robustness of Student Models via Adaptive Adversarial Distillation

1 code implementation CVPR 2023 Bo Huang, Mingyang Chen, Yi Wang, Junda Lu, Minhao Cheng, Wei Wang

Thus, recent studies concern about adversarial distillation (AD) that aims to inherit not only prediction accuracy but also adversarial robustness of a robust teacher model under the paradigm of robust optimization.

Adversarial Robustness Knowledge Distillation

Tele-Knowledge Pre-training for Fault Analysis

1 code implementation20 Oct 2022 Zhuo Chen, Wen Zhang, Yufeng Huang, Mingyang Chen, Yuxia Geng, Hongtao Yu, Zhen Bi, Yichi Zhang, Zhen Yao, Wenting Song, Xinliang Wu, Yi Yang, Mingyi Chen, Zhaoyang Lian, YingYing Li, Lei Cheng, Huajun Chen

In this work, we share our experience on tele-knowledge pre-training for fault analysis, a crucial task in telecommunication applications that requires a wide range of knowledge normally found in both machine log data and product documents.

Language Modelling

Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting

1 code implementation10 May 2022 Mingyang Chen, Wen Zhang, Zhen Yao, Xiangnan Chen, Mengxiao Ding, Fei Huang, Huajun Chen

We study the knowledge extrapolation problem to embed new components (i. e., entities and relations) that come with emerging knowledge graphs (KGs) in the federated setting.

Knowledge Graphs Link Prediction +1

Knowledge Graph Embedding in E-commerce Applications: Attentive Reasoning, Explanations, and Transferable Rules

no code implementations16 Dec 2021 Wen Zhang, Shumin Deng, Mingyang Chen, Liang Wang, Qiang Chen, Feiyu Xiong, Xiangwen Liu, Huajun Chen

We first identity three important desiderata for e-commerce KG systems: 1) attentive reasoning, reasoning over a few target relations of more concerns instead of all; 2) explanation, providing explanations for a prediction to help both users and business operators understand why the prediction is made; 3) transferable rules, generating reusable rules to accelerate the deployment of a KG to new systems.

Entity Embeddings Graph Attention +4

Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding

1 code implementation27 Oct 2021 Mingyang Chen, Wen Zhang, Yushan Zhu, Hongting Zhou, Zonggang Yuan, Changliang Xu, Huajun Chen

In this paper, to achieve inductive knowledge graph embedding, we propose a model MorsE, which does not learn embeddings for entities but learns transferable meta-knowledge that can be used to produce entity embeddings.

Entity Embeddings Inductive Relation Prediction +6

Explaining Knowledge Graph Embedding via Latent Rule Learning

no code implementations29 Sep 2021 Wen Zhang, Mingyang Chen, Zezhong Xu, Yushan Zhu, Huajun Chen

KGExplainer is a multi-hop reasoner learning latent rules for link prediction and is encouraged to behave similarly to KGEs during prediction through knowledge distillation.

Knowledge Distillation Knowledge Graph Embedding +3

Towards Principled Representation Learning for Entity Alignment

no code implementations1 Jan 2021 Lingbing Guo, Zequn Sun, Mingyang Chen, Wei Hu, Huajun Chen

In this paper, we define a typical paradigm abstracted from the existing methods, and analyze how the representation discrepancy between two potentially-aligned entities is implicitly bounded by a predefined margin in the scoring function for embedding learning.

Entity Alignment Machine Translation +1

FedE: Embedding Knowledge Graphs in Federated Setting

2 code implementations24 Oct 2020 Mingyang Chen, Wen Zhang, Zonggang Yuan, Yantao Jia, Huajun Chen

Knowledge graphs (KGs) consisting of triples are always incomplete, so it's important to do Knowledge Graph Completion (KGC) by predicting missing triples.

Knowledge Graph Embedding Knowledge Graph Embeddings

DualDE: Dually Distilling Knowledge Graph Embedding for Faster and Cheaper Reasoning

no code implementations13 Sep 2020 Yushan Zhu, Wen Zhang, Mingyang Chen, Hui Chen, Xu Cheng, Wei zhang, Huajun Chen

In DualDE, we propose a soft label evaluation mechanism to adaptively assign different soft label and hard label weights to different triples, and a two-stage distillation approach to improve the student's acceptance of the teacher.

Knowledge Distillation Knowledge Graph Embedding +2

Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs

1 code implementation IJCNLP 2019 Mingyang Chen, Wen Zhang, Wei zhang, Qiang Chen, Huajun Chen

Link prediction is an important way to complete knowledge graphs (KGs), while embedding-based methods, effective for link prediction in KGs, perform poorly on relations that only have a few associative triples.

Knowledge Graphs Link Prediction +1

HAKE: Human Activity Knowledge Engine

2 code implementations13 Apr 2019 Yong-Lu Li, Liang Xu, Xinpeng Liu, Xijie Huang, Yue Xu, Mingyang Chen, Ze Ma, Shiyi Wang, Hao-Shu Fang, Cewu Lu

To address these and promote the activity understanding, we build a large-scale Human Activity Knowledge Engine (HAKE) based on the human body part states.

Ranked #2 on Human-Object Interaction Detection on HICO (using extra training data)

Action Detection Human-Object Interaction Detection +1

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