Search Results for author: Mingming Sun

Found 31 papers, 7 papers with code

OIE@OIA: an Adaptable and Efficient Open Information Extraction Framework

no code implementations ACL 2022 Xin Wang, Minlong Peng, Mingming Sun, Ping Li

OIE@OIA follows the methodology of Open Information eXpression (OIX): parsing a sentence to an Open Information Annotation (OIA) Graph and then adapting the OIA graph to different OIE tasks with simple rules.

Open Information Extraction Sentence

A Predicate-Function-Argument Annotation of Natural Language for Open-Domain Information eXpression

no code implementations EMNLP 2020 Mingming Sun, Wenyue Hua, Zoey Liu, Xin Wang, Kangjie Zheng, Ping Li

Based on the same platform of OIX, the OIE strategies are reusable, and people can select a set of strategies to assemble their algorithm for a specific task so that the adaptability may be significantly increased.

Open Information Extraction Sentence

Multi-Hop Open-Domain Question Answering over Structured and Unstructured Knowledge

no code implementations Findings (NAACL) 2022 Yue Feng, Zhen Han, Mingming Sun, Ping Li

DEHG employs a graph constructor to integrate structured and unstructured information, a context encoder to represent nodes and question, a heterogeneous information reasoning layer to conduct multi-hop reasoning on both information sources, and an answer decoder to generate answers for the question.

Open-Domain Question Answering

MQuinE: a cure for "Z-paradox" in knowledge graph embedding models

no code implementations5 Feb 2024 Yang Liu, Huang Fang, Yunfeng Cai, Mingming Sun

Knowledge graph embedding (KGE) models achieved state-of-the-art results on many knowledge graph tasks including link prediction and information retrieval.

Information Retrieval Knowledge Graph Embedding +3

HiCAST: Highly Customized Arbitrary Style Transfer with Adapter Enhanced Diffusion Models

no code implementations11 Jan 2024 Hanzhang Wang, Haoran Wang, Jinze Yang, Zhongrui Yu, Zeke Xie, Lei Tian, Xinyan Xiao, Junjun Jiang, Xianming Liu, Mingming Sun

In the specific, our model is constructed based on Latent Diffusion Model (LDM) and elaborately designed to absorb content and style instance as conditions of LDM.

Style Transfer

S3IM: Stochastic Structural SIMilarity and Its Unreasonable Effectiveness for Neural Fields

1 code implementation ICCV 2023 Zeke Xie, Xindi Yang, Yujie Yang, Qi Sun, Yixiang Jiang, Haoran Wang, Yunfeng Cai, Mingming Sun

Recently, Neural Radiance Field (NeRF) has shown great success in rendering novel-view images of a given scene by learning an implicit representation with only posed RGB images.

Novel View Synthesis Surface Reconstruction

A Semi-Autoregressive Graph Generative Model for Dependency Graph Parsing

no code implementations21 Jun 2023 Ye Ma, Mingming Sun, Ping Li

And the latter assumes these components to be independent so that they can be outputted in a one-shot manner.

Dependency Parsing Graph Generation

A Graph-Guided Reasoning Approach for Open-ended Commonsense Question Answering

no code implementations18 Mar 2023 Zhen Han, Yue Feng, Mingming Sun

Hence, a new benchmark challenge set for open-ended commonsense reasoning (OpenCSR) has been recently released, which contains natural science questions without any predefined choices.

Multiple-choice Question Answering +1

NL2GDPR: Automatically Develop GDPR Compliant Android Application Features from Natural Language

no code implementations29 Aug 2022 Faysal Hossain Shezan, Yingjie Lao, Minlong Peng, Xin Wang, Mingming Sun, Ping Li

At the core, NL2GDPR is a privacy-centric information extraction model, appended with a GDPR policy finder and a policy generator.

CGAR: Critic Guided Action Redistribution in Reinforcement Leaning

1 code implementation23 Jun 2022 Tairan Huang, Xu Li, Hao Li, Mingming Sun, Ping Li

As discussed in this paper, under the settings of the off-policy actor critic algorithms, we demonstrate that the critic can bring more expected discounted rewards than or at least equal to the actor.

Reinforcement Learning (RL)

Dataset Pruning: Reducing Training Data by Examining Generalization Influence

no code implementations19 May 2022 Shuo Yang, Zeke Xie, Hanyu Peng, Min Xu, Mingming Sun, Ping Li

To answer these, we propose dataset pruning, an optimization-based sample selection method that can (1) examine the influence of removing a particular set of training samples on model's generalization ability with theoretical guarantee, and (2) construct the smallest subset of training data that yields strictly constrained generalization gap.

Joint learning of object graph and relation graph for visual question answering

no code implementations9 May 2022 Hao Li, Xu Li, Belhal Karimi, Jie Chen, Mingming Sun

Modeling visual question answering(VQA) through scene graphs can significantly improve the reasoning accuracy and interpretability.

Attribute Question Answering +2

SpaceE: Knowledge Graph Embedding by Relational Linear Transformation in the Entity Space

no code implementations21 Apr 2022 Jinxing Yu, Yunfeng Cai, Mingming Sun, Ping Li

Translation distance based knowledge graph embedding (KGE) methods, such as TransE and RotatE, model the relation in knowledge graphs as translation or rotation in the vector space.

Knowledge Graph Embedding Knowledge Graphs +3

Label-Smoothed Backdoor Attack

no code implementations19 Feb 2022 Minlong Peng, Zidi Xiong, Mingming Sun, Ping Li

In order to achieve a high attack success rate using as few poisoned training samples as possible, most existing attack methods change the labels of the poisoned samples to the target class.

Backdoor Attack

On the Power-Law Hessian Spectrums in Deep Learning

no code implementations31 Jan 2022 Zeke Xie, Qian-Yuan Tang, Yunfeng Cai, Mingming Sun, Ping Li

It is well-known that the Hessian of deep loss landscape matters to optimization, generalization, and even robustness of deep learning.

Causal Discovery with Flow-based Conditional Density Estimation

1 code implementation ICDM 21 2021 Shaogang Ren, Haiyan Yin, Mingming Sun, Ping Li

Then we formulate a novel evaluation metric to infer the scores for each potential causal direction based on the variance of the conditional density estimation.

Causal Discovery Density Estimation

Optimal Transport for Long-Tailed Recognition with Learnable Cost Matrix

no code implementations ICLR 2022 Hanyu Peng, Mingming Sun, Ping Li

It is attracting attention to the long-tailed recognition problem, a burning issue that has become very popular recently.

Long-tail Learning

Lifting Imbalanced Regression with Self-Supervised Learning

no code implementations29 Sep 2021 Weiguo Pian, Hanyu Peng, Mingming Sun, Ping Li

In this paper, we work on a seamless marriage of imbalanced regression and self-supervised learning.

imbalanced classification regression +1

Causal Discovery via Cholesky Factorization

no code implementations29 Sep 2021 Xu Li, Yunfeng Cai, Mingming Sun, Ping Li

Discovering the causal relationship via recovering the directed acyclic graph (DAG) structure from the observed data is a challenging combinatorial problem.

Causal Discovery

S$^2$-MLPv2: Improved Spatial-Shift MLP Architecture for Vision

3 code implementations2 Aug 2021 Tan Yu, Xu Li, Yunfeng Cai, Mingming Sun, Ping Li

More recently, using smaller patches with a pyramid structure, Vision Permutator (ViP) and Global Filter Network (GFNet) achieve better performance than S$^2$-MLP.

Inductive Bias

Rethinking Token-Mixing MLP for MLP-based Vision Backbone

no code implementations28 Jun 2021 Tan Yu, Xu Li, Yunfeng Cai, Mingming Sun, Ping Li

By introducing the inductive bias from the image processing, convolution neural network (CNN) has achieved excellent performance in numerous computer vision tasks and has been established as \emph{de facto} backbone.

Inductive Bias

S$^2$-MLP: Spatial-Shift MLP Architecture for Vision

1 code implementation14 Jun 2021 Tan Yu, Xu Li, Yunfeng Cai, Mingming Sun, Ping Li

We discover that the token-mixing MLP is a variant of the depthwise convolution with a global reception field and spatial-specific configuration.

Learning Interpretable Relationships between Entities, Relations and Concepts via Bayesian Structure Learning on Open Domain Facts

no code implementations ACL 2020 Jingyuan Zhang, Mingming Sun, Yue Feng, Ping Li

Compared to the state-of-the-art methods, the learned network structures help improving the identification of concepts for entities based on the relations of entities on both datasets.

Distributed Hierarchical GPU Parameter Server for Massive Scale Deep Learning Ads Systems

2 code implementations12 Mar 2020 Weijie Zhao, Deping Xie, Ronglai Jia, Yulei Qian, Ruiquan Ding, Mingming Sun, Ping Li

For example, a sponsored online advertising system can contain more than $10^{11}$ sparse features, making the neural network a massive model with around 10 TB parameters.

Reinforced Product Metadata Selection for Helpfulness Assessment of Customer Reviews

no code implementations IJCNLP 2019 Miao Fan, Chao Feng, Mingming Sun, Ping Li

Given a product, a selector (agent) learns from both the keys in the product metadata and one of its reviews to take an action that selects the correct value, and a successive predictor (network) makes the free-text review attend to this value to obtain better neural representations for helpfulness assessment.

Logician: A Unified End-to-End Neural Approach for Open-Domain Information Extraction

no code implementations29 Apr 2019 Mingming Sun, Xu Li, Xin Wang, Miao Fan, Yue Feng, Ping Li

In this paper, we consider the problem of open information extraction (OIE) for extracting entity and relation level intermediate structures from sentences in open-domain.

Attribute Open Information Extraction +3

Logician and Orator: Learning from the Duality between Language and Knowledge in Open Domain

no code implementations EMNLP 2018 Mingming Sun, Xu Li, Ping Li

We propose the task of Open-Domain Information Narration (OIN) as the reverse task of Open Information Extraction (OIE), to implement the dual structure between language and knowledge in the open domain.

Open Information Extraction reinforcement-learning +2

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