Search Results for author: Li Zeng

Found 14 papers, 4 papers with code

EFSA: Towards Event-Level Financial Sentiment Analysis

no code implementations8 Apr 2024 Tianyu Chen, Yiming Zhang, Guoxin Yu, Dapeng Zhang, Li Zeng, Qing He, Xiang Ao

In this paper, we extend financial sentiment analysis~(FSA) to event-level since events usually serve as the subject of the sentiment in financial text.

Benchmarking Event Extraction +1

Satellite Federated Edge Learning: Architecture Design and Convergence Analysis

no code implementations2 Apr 2024 Yuanming Shi, Li Zeng, Jingyang Zhu, Yong Zhou, Chunxiao Jiang, Khaled B. Letaief

Although promising, the dynamics of LEO networks, characterized by the high mobility of satellites and short ground-to-satellite link (GSL) duration, pose unique challenges for FEEL.

LocMoE: A Low-Overhead MoE for Large Language Model Training

no code implementations25 Jan 2024 Jing Li, Zhijie Sun, Xuan He, Li Zeng, Yi Lin, Entong Li, Binfan Zheng, Rongqian Zhao, Xin Chen

However, the performance of MoE is limited by load imbalance and high latency of All-to-All communication, along with relatively redundant computation owing to large expert capacity.

Language Modelling Large Language Model

Key Gene Mining in Transcriptional Regulation for Specific Biological Processes with Small Sample Sizes Using Multi-network pipeline Transformer

no code implementations7 Aug 2023 Kerui Huang, Jianhong Tian, Lei Sun, Li Zeng, Peng Xie, Aihua Deng, Ping Mo, Zhibo Zhou, Ming Jiang, Yun Wang, Xiaocheng Jiang

Gene mining is an important topic in the field of life sciences, but traditional machine learning methods cannot consider the regulatory relationships between genes.

Data Augmentation

Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction

1 code implementation8 Jun 2023 Xuan Lin, Lichang Dai, Yafang Zhou, Zu-Guo Yu, Wen Zhang, Jian-Yu Shi, Dong-Sheng Cao, Li Zeng, Haowen Chen, Bosheng Song, Philip S. Yu, Xiangxiang Zeng

Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs).

Drug Discovery Graph Learning +1

Bounded KRnet and its applications to density estimation and approximation

no code implementations15 May 2023 Li Zeng, Xiaoliang Wan, Tao Zhou

In this paper, we develop an invertible mapping, called B-KRnet, on a bounded domain and apply it to density estimation/approximation for data or the solutions of PDEs such as the Fokker-Planck equation and the Keller-Segel equation.

Density Estimation

Gradient-enhanced deep neural network approximations

no code implementations8 Nov 2022 Xiaodong Feng, Li Zeng

We propose in this work the gradient-enhanced deep neural networks (DNNs) approach for function approximations and uncertainty quantification.

Uncertainty Quantification

Adaptive deep density approximation for fractional Fokker-Planck equations

no code implementations26 Oct 2022 Li Zeng, Xiaoliang Wan, Tao Zhou

To this end, we represent the solution with an explicit PDF model induced by a flow-based deep generative model, simplified KRnet, which constructs a transport map from a simple distribution to the target distribution.

Solving time dependent Fokker-Planck equations via temporal normalizing flow

no code implementations28 Dec 2021 Xiaodong Feng, Li Zeng, Tao Zhou

In this work, we propose an adaptive learning approach based on temporal normalizing flows for solving time-dependent Fokker-Planck (TFP) equations.

SegTime: Precise Time Series Segmentation without Sliding Window

no code implementations29 Sep 2021 Li Zeng, Baifan Zhou, Mohammad Al-Rifai, Evgeny Kharlamov

We propose a neural networks approach SegTime that finds precise breakpoints, obviates sliding windows, handles long-term dependencies, and it is insensitive to the label changing frequency.

Human Activity Recognition Segmentation +1

A general kernel boosting framework integrating pathways for predictive modeling based on genomic data

1 code implementation26 Aug 2020 Li Zeng, Zhaolong Yu, Yiliang Zhang, Hongyu Zhao

Predictive modeling based on genomic data has gained popularity in biomedical research and clinical practice by allowing researchers and clinicians to identify biomarkers and tailor treatment decisions more efficiently.

Learning to Identify High Betweenness Centrality Nodes from Scratch: A Novel Graph Neural Network Approach

1 code implementation24 May 2019 Changjun Fan, Li Zeng, Yuhui Ding, Muhao Chen, Yizhou Sun, Zhong Liu

By training on small-scale networks, the learned model is capable of assigning relative BC scores to nodes for any unseen networks, and thus identifying the highly-ranked nodes.

Community Detection Decoder

A pathway-based kernel boosting method for sample classification using genomic data

1 code implementation11 Mar 2018 Li Zeng, Zhaolong Yu, Hongyu Zhao

Most of the methods focus on testing marginal significance of the associations between pathways and clinical phenotypes.

General Classification

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