Peng Li

Multi-sensor GIW-PHD filter for multiple extended target tracking

2015

Gaussian inverse Wishart probability hypothesis density (GIW-PHD) filter has proven to be a promising algorithm for multiple extended target tracking with shape estimation. However, as far as I know, this method only can be used …

Application of expert system in mine power supply network fault diagnosis

2017

The expert system is applied to the fault diagnosis of mine power supply network, and a way to realize the fault diagnosis expert system of mine power supply network is put forward. In this paper, …

Temporal Item Embedding with Static Similarity Regularization for Sequential Recommendation

2018

Recommender systems have attracted a significant amount of research interests in recent years. Traditional methods such as content-based approaches and collaborative filtering approaches mainly focus on modeling the general user preference by using the user's …

Dependable Deep Computation Model for Feature Learning on Big Data in Cyber-Physical Systems

2018 · ACM Transactions on Cyber-Physical Systems

With the ongoing development of sensor devices and network techniques, big data are being generated from the cyber-physical systems. Because of sensor equipment occasional failure and network transmission unreliability, a large number of low-quality data, …

Secure Balance Planning of Off-blockchain Payment Channel Networks

2020

Off-blockchain payment channels can significantly improve blockchain scalability by enabling a large number of micro-payments between two blockchain nodes, without committing every single payment to the blockchain. Multiple payment channels form a payment network, so …

Disentangle-based Continual Graph Representation Learning

2020 · arXiv (Cornell University)

Graph embedding (GE) methods embed nodes (and/or edges) in graph into a low-dimensional semantic space, and have shown its effectiveness in modeling multi-relational data. However, existing GE models are not practical in real-world applications since …

Learning from Context or Names? An Empirical Study on Neural Relation Extraction

2020

Neural models have achieved remarkable success on relation extraction (RE) benchmarks. However, there is no clear understanding which type of information affects existing RE models to make decisions and how to further improve the performance …

MAVEN: A Massive General Domain Event Detection Dataset

2020

Xiaozhi Wang, Ziqi Wang, Xu Han, Wangyi Jiang, Rong Han, Zhiyuan Liu, Juanzi Li, Peng Li, Yankai Lin, Jie Zhou. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020.

Glint: Decentralized Federated Graph Learning with Traffic Throttling and Flow Scheduling

2021

Federated learning has been proposed as a promising distributed machine learning paradigm with strong privacy protection on training data. Existing work mainly focuses on training convolutional neural network (CNN) models good at learning on image/voice …

A Template-based Method for Constrained Neural Machine Translation

2022 · arXiv (Cornell University)

Machine translation systems are expected to cope with various types of constraints in many practical scenarios. While neural machine translation (NMT) has achieved strong performance in unconstrained cases, it is non-trivial to impose pre-specified constraints …

A Partitioning Method for Gamma Gaussian inverse Wishart Probability Hypothesis Density Filter using Kolmogorov-Smirnov Test

2021 · 2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)

In multiple extended objects tracking, objects produce more than one measurement per time step. When objects are spatially close or maneuvering, the performance of typical partitioning methods will be reduced. This paper presents a partitioning …

MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction

2022 · arXiv (Cornell University)

The diverse relationships among real-world events, including coreference, temporal, causal, and subevent relations, are fundamental to understanding natural languages. However, two drawbacks of existing datasets limit event relation extraction (ERE) tasks: (1) Small scale. Due …

The collaborative role of blockchain, artificial intelligence, and industrial internet of things in digitalization of small and medium-size enterprises

2023 · Scientific Reports

Due to digitalization, small and medium-sized enterprises (SMEs) have significantly enhanced their efficiency and productivity in the past few years. The process to automate SME transaction execution is getting highly multifaceted as the number of …

Plug-and-Play Knowledge Injection for Pre-trained Language Models

2023 · arXiv (Cornell University)

Injecting external knowledge can improve the performance of pre-trained language models (PLMs) on various downstream NLP tasks. However, massive retraining is required to deploy new knowledge injection methods or knowledge bases for downstream tasks. In …

DiffPrep: Differentiable Data Preprocessing Pipeline Search for Learning over Tabular Data

2023 · Proceedings of the ACM on Management of Data

Data preprocessing is a crucial step in the machine learning process that transforms raw data into a more usable format for downstream ML models. However, it can be costly and time-consuming, often requiring the expertise …

Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory

2023 · arXiv (Cornell University)

Risk-sensitive reinforcement learning (RL) has garnered significant attention in recent years due to the growing interest in deploying RL agents in real-world scenarios. A critical aspect of risk awareness involves modeling highly rare risk events …

Radar Intra-Pulse Signal Modulation Classification Based on Omni-Dimensional Dynamic Convolution

2023

In this work, the omni-dimensional dynamic convolution (ODConv) layer based network (OD-CNN) with focal loss function is applied to the radar intra-pulse signal modulation classification, which greatly improves the classification accuracy. Compared to the convolution …

ToolRerank: Adaptive and Hierarchy-Aware Reranking for Tool Retrieval

2024 · arXiv (Cornell University)

Tool learning aims to extend the capabilities of large language models (LLMs) with external tools. A major challenge in tool learning is how to support a large number of tools, including unseen tools. To address …

Channel prediction method based on the data-driving for distribution automation main station

2024 · Frontiers in Energy Research

A data-driven channel prediction method for distribution automation master is proposed to address the poor quality of communication network and communication system transmission problems in distribution network communication. In this paper, an adaptive broad learning …

Federating to Grow Transformers with Constrained Resources without Model Sharing

2024 · arXiv (Cornell University)

The high resource consumption of large-scale models discourages resource-constrained users from developing their customized transformers. To this end, this paper considers a federated framework named Fed-Grow for multiple participants to cooperatively scale a transformer from …

DocRED: A Large-Scale Document-Level Relation Extraction Dataset

2019

Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, Maosong Sun. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019.

Deep Recurrent Models with Fast-Forward Connections for Neural Machine Translation

2016 · Transactions of the Association for Computational Linguistics

Neural machine translation (NMT) aims at solving machine translation (MT) problems using neural networks and has exhibited promising results in recent years. However, most of the existing NMT models are shallow and there is still …

FewRel 2.0: Towards More Challenging Few-Shot Relation Classification

2019

Tianyu Gao, Xu Han, Hao Zhu, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language …