Search Results for author: Jingbo Zhou

Found 39 papers, 15 papers with code

Improving Retrieval Augmented Language Model with Self-Reasoning

no code implementations29 Jul 2024 Yuan Xia, Jingbo Zhou, Zhenhui Shi, Jun Chen, Haifeng Huang

The Retrieval-Augmented Language Model (RALM) has shown remarkable performance on knowledge-intensive tasks by incorporating external knowledge during inference, which mitigates the factual hallucinations inherited in large language models (LLMs).

Fact Verification Language Modelling +2

Warming Up Cold-Start CTR Prediction by Learning Item-Specific Feature Interactions

no code implementations14 Jul 2024 Yaqing Wang, Hongming Piao, daxiang dong, Quanming Yao, Jingbo Zhou

While existing methods focus on enhancing item ID embeddings for new items within general CTR models, they tend to adopt a global feature interaction approach, often overshadowing new items with sparse data by those with abundant interactions.

Click-Through Rate Prediction Graph Neural Network +1

Unifying Sequences, Structures, and Descriptions for Any-to-Any Protein Generation with the Large Multimodal Model HelixProtX

1 code implementation12 Jul 2024 ZhiYuan Chen, Tianhao Chen, Chenggang Xie, Yang Xue, Xiaonan Zhang, Jingbo Zhou, Xiaomin Fang

The experimental results affirm the advanced capabilities of HelixProtX, not only in generating functional descriptions from amino acid sequences but also in executing critical tasks such as designing protein sequences and structures from textual descriptions.

scientific discovery

NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics

no code implementations16 Jun 2024 Jingbo Zhou, Shaorong Chen, Jun Xia, Sizhe Liu, Tianze Ling, Wenjie Du, Yue Liu, Jianwei Yin, Stan Z. Li

In this work, we present the first unified benchmark NovoBench for \emph{de novo} peptide sequencing, which comprises diverse mass spectrum data, integrated models, and comprehensive evaluation metrics.

Benchmarking de novo peptide sequencing

Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning

no code implementations28 Mar 2024 Ji Liu, Chunlu Chen, Yu Li, Lin Sun, Yulun Song, Jingbo Zhou, Bo Jing, Dejing Dou

While centralized servers pose a risk of being a single point of failure, decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities.

Distributed Computing Federated Learning +1

A Framework for Cost-Effective and Self-Adaptive LLM Shaking and Recovery Mechanism

no code implementations12 Mar 2024 Zhiyu Chen, Yu Li, Suochao Zhang, Jingbo Zhou, Jiwen Zhou, Chenfu Bao, dianhai yu

As Large Language Models (LLMs) gain great success in real-world applications, an increasing number of users are seeking to develop and deploy their customized LLMs through cloud services.

Privacy Preserving

Pre-Training on Large-Scale Generated Docking Conformations with HelixDock to Unlock the Potential of Protein-ligand Structure Prediction Models

no code implementations21 Oct 2023 Lihang Liu, Shanzhuo Zhang, Donglong He, Xianbin Ye, Jingbo Zhou, Xiaonan Zhang, Yaoyao Jiang, Weiming Diao, Hang Yin, Hua Chai, Fan Wang, Jingzhou He, Liang Zheng, Yonghui Li, Xiaomin Fang

In this work, we show that by pre-training on a large-scale docking conformation generated by traditional physics-based docking tools and then fine-tuning with a limited set of experimentally validated receptor-ligand complexes, we can obtain a protein-ligand structure prediction model with outstanding performance.

Drug Discovery Molecular Docking

LLM for SoC Security: A Paradigm Shift

no code implementations9 Oct 2023 Dipayan Saha, Shams Tarek, Katayoon Yahyaei, Sujan Kumar Saha, Jingbo Zhou, Mark Tehranipoor, Farimah Farahmandi

As the ubiquity and complexity of system-on-chip (SoC) designs increase across electronic devices, the task of incorporating security into an SoC design flow poses significant challenges.

Natural Language Understanding Program Synthesis

Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Network

1 code implementation31 Aug 2023 Weijia Zhang, Le Zhang, Jindong Han, Hao liu, Yanjie Fu, Jingbo Zhou, Yu Mei, Hui Xiong

Accurate traffic forecasting is crucial for the development of Intelligent Transportation Systems (ITS), playing a pivotal role in modern urban traffic management.

Time Series Time Series Forecasting

Tuning Pre-trained Model via Moment Probing

1 code implementation ICCV 2023 Mingze Gao, Qilong Wang, Zhenyi Lin, Pengfei Zhu, QinGhua Hu, Jingbo Zhou

Distinguished from LP which builds a linear classification head based on the mean of final features (e. g., word tokens for ViT) or classification tokens, our MP performs a linear classifier on feature distribution, which provides the stronger representation ability by exploiting richer statistical information inherent in features.

Image Classification

Spatial Heterophily Aware Graph Neural Networks

1 code implementation21 Jun 2023 Congxi Xiao, Jingbo Zhou, Jizhou Huang, Tong Xu, Hui Xiong

However, urban graphs usually can be observed to possess a unique spatial heterophily property; that is, the dissimilarity of neighbors at different spatial distances can exhibit great diversity.

Diversity Graph Neural Network

Multi-Temporal Relationship Inference in Urban Areas

1 code implementation15 Jun 2023 Shuangli Li, Jingbo Zhou, Ji Liu, Tong Xu, Enhong Chen, Hui Xiong

Specifically, we propose a solution to Trial with a graph learning scheme, which includes a spatially evolving graph neural network (SEENet) with two collaborative components: spatially evolving graph convolution module (SEConv) and spatially evolving self-supervised learning strategy (SE-SSL).

Graph Learning Graph Neural Network +2

Deep Graph Neural Networks via Posteriori-Sampling-based Node-Adaptive Residual Module

1 code implementation9 May 2023 Jingbo Zhou, Yixuan Du, Ruqiong Zhang, Jun Xia, Zhizhi Yu, Zelin Zang, Di Jin, Carl Yang, Rui Zhang, Stan Z. Li

Additionally, we reveal the drawbacks of previous residual methods, such as the lack of node adaptability and severe loss of high-order neighborhood subgraph information, and propose a \textbf{Posterior-Sampling-based, Node-Adaptive Residual module (PSNR)}.

Node Classification

Adaptive Depth Graph Attention Networks

no code implementations16 Jan 2023 Jingbo Zhou, Yixuan Du, Ruqiong Zhang, Rui Zhang

As one of the most popular GNN architectures, the graph attention networks (GAT) is considered the most advanced learning architecture for graph representation and has been widely used in various graph mining tasks with impressive results.

Graph Attention Graph Mining

Towards Table-to-Text Generation with Pretrained Language Model: A Table Structure Understanding and Text Deliberating Approach

1 code implementation5 Jan 2023 Miao Chen, Xinjiang Lu, Tong Xu, Yanyan Li, Jingbo Zhou, Dejing Dou, Hui Xiong

Although remarkable progress on the neural table-to-text methods has been made, the generalization issues hinder the applicability of these models due to the limited source tables.

Decoder Descriptive +2

Human-instructed Deep Hierarchical Generative Learning for Automated Urban Planning

no code implementations1 Dec 2022 Dongjie Wang, Lingfei Wu, Denghui Zhang, Jingbo Zhou, Leilei Sun, Yanjie Fu

The third stage is to leverage multi-attentions to model the zone-zone peer dependencies of the functionality projections to generate grid-level land-use configurations.

A Contextual Master-Slave Framework on Urban Region Graph for Urban Village Detection

no code implementations26 Nov 2022 Congxi Xiao, Jingbo Zhou, Jizhou Huang, HengShu Zhu, Tong Xu, Dejing Dou, Hui Xiong

The core idea of such a framework is to firstly pre-train a basis (or master) model over the URG, and then to adaptively derive specific (or slave) models from the basis model for different regions.

Specificity

Multi-Job Intelligent Scheduling with Cross-Device Federated Learning

no code implementations24 Nov 2022 Ji Liu, Juncheng Jia, Beichen Ma, Chendi Zhou, Jingbo Zhou, Yang Zhou, Huaiyu Dai, Dejing Dou

The system model enables a parallel training process of multiple jobs, with a cost model based on the data fairness and the training time of diverse devices during the parallel training process.

Bayesian Optimization Fairness +2

Robust Training of Graph Neural Networks via Noise Governance

1 code implementation12 Nov 2022 Siyi Qian, Haochao Ying, Renjun Hu, Jingbo Zhou, Jintai Chen, Danny Z. Chen, Jian Wu

To address these issues, we propose a novel RTGNN (Robust Training of Graph Neural Networks via Noise Governance) framework that achieves better robustness by learning to explicitly govern label noise.

Memorization

SDWPF: A Dataset for Spatial Dynamic Wind Power Forecasting Challenge at KDD Cup 2022

1 code implementation8 Aug 2022 Jingbo Zhou, Xinjiang Lu, Yixiong Xiao, Jiantao Su, Junfu Lyu, Yanjun Ma, Dejing Dou

Thus, Wind Power Forecasting (WPF) has been widely recognized as one of the most critical issues in wind power integration and operation.

Structure-aware Protein Self-supervised Learning

1 code implementation6 Apr 2022 Can Chen, Jingbo Zhou, Fan Wang, Xue Liu, Dejing Dou

Furthermore, we propose to leverage the available protein language model pretrained on protein sequences to enhance the self-supervised learning.

Graph Neural Network Protein Language Model +2

GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Prediction

1 code implementation24 Sep 2021 Shuangli Li, Jingbo Zhou, Tong Xu, Dejing Dou, Hui Xiong

Though graph contrastive learning (GCL) methods have achieved extraordinary performance with insufficient labeled data, most focused on designing data augmentation schemes for general graphs.

Contrastive Learning Data Augmentation +4

Adversarial Neural Trip Recommendation

no code implementations24 Sep 2021 Linlang Jiang, Jingbo Zhou, Tong Xu, Yanyan Li, Hao Chen, Jizhou Huang, Hui Xiong

To that end, we propose an Adversarial Neural Trip Recommendation (ANT) framework to tackle the above challenges.

Decoder Recommendation Systems

Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity

1 code implementation21 Jul 2021 Shuangli Li, Jingbo Zhou, Tong Xu, Liang Huang, Fan Wang, Haoyi Xiong, Weili Huang, Dejing Dou, Hui Xiong

To this end, we propose a structure-aware interactive graph neural network (SIGN) which consists of two components: polar-inspired graph attention layers (PGAL) and pairwise interactive pooling (PiPool).

Drug Discovery Graph Attention +2

Spatial Object Recommendation with Hints: When Spatial Granularity Matters

no code implementations8 Jan 2021 Hui Luo, Jingbo Zhou, Zhifeng Bao, Shuangli Li, J. Shane Culpepper, Haochao Ying, Hao liu, Hui Xiong

We design a novel multi-task learning model called MPR (short for Multi-level POI Recommendation), where each task aims to return the top-k POIs at a certain spatial granularity level.

Attribute Multi-Task Learning +2

C-Watcher: A Framework for Early Detection of High-Risk Neighborhoods Ahead of COVID-19 Outbreak

no code implementations22 Dec 2020 Congxi Xiao, Jingbo Zhou, Jizhou Huang, An Zhuo, Ji Liu, Haoyi Xiong, Dejing Dou

Furthermore, to transfer the firsthand knowledge (witted in epicenters) to the target city before local outbreaks, we adopt a novel adversarial encoder framework to learn "city-invariant" representations from the mobility-related features for precise early detection of high-risk neighborhoods, even before any confirmed cases known, in the target city.

Distance-aware Molecule Graph Attention Network for Drug-Target Binding Affinity Prediction

1 code implementation17 Dec 2020 Jingbo Zhou, Shuangli Li, Liang Huang, Haoyi Xiong, Fan Wang, Tong Xu, Hui Xiong, Dejing Dou

The hierarchical attentive aggregation can capture spatial dependencies among atoms, as well as fuse the position-enhanced information with the capability of discriminating multiple spatial relations among atoms.

Drug Discovery Graph Attention +2

Defending Water Treatment Networks: Exploiting Spatio-temporal Effects for Cyber Attack Detection

no code implementations26 Aug 2020 Dongjie Wang, Pengyang Wang, Jingbo Zhou, Leilei Sun, Bowen Du, Yanjie Fu

To this end, we propose a structured anomaly detection framework to defend WTNs by modeling the spatio-temporal characteristics of cyber attacks in WTNs.

Anomaly Detection Attribute +2

Polestar: An Intelligent, Efficient and National-Wide Public Transportation Routing Engine

no code implementations11 Jul 2020 Hao Liu, Ying Li, Yanjie Fu, Huaibo Mei, Jingbo Zhou, Xu Ma, Hui Xiong

Then, we introduce a general route search algorithm coupled with an efficient station binding method for efficient route candidate generation.

Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Prediction

1 code implementation24 Nov 2019 Weijia Zhang, Hao liu, Yanchi Liu, Jingbo Zhou, Hui Xiong

However, it is a non-trivial task for predicting citywide parking availability because of three major challenges: 1) the non-Euclidean spatial autocorrelation among parking lots, 2) the dynamic temporal autocorrelation inside of and between parking lots, and 3) the scarcity of information about real-time parking availability obtained from real-time sensors (e. g., camera, ultrasonic sensor, and GPS).

Clustering Graph Neural Network

Store Location Selection via Mining Search Query Logs of Baidu Maps

no code implementations12 Jun 2016 Mengwen Xu, Tianyi Wang, Zhengwei Wu, Jingbo Zhou, Jian Li, Haishan Wu

In this paper, we propose a Demand Distribution Driven Store Placement (D3SP) framework for business store placement by mining search query data from Baidu Maps.

Clustering

A Generic Inverted Index Framework for Similarity Search on the GPU - Technical Report

1 code implementation28 Mar 2016 Jingbo Zhou, Qi Guo, H. V. Jagadish, Luboš Krčál, Siyuan Liu, Wenhao Luan, Anthony K. H. Tung, Yueji Yang, Yuxin Zheng

We propose a novel generic inverted index framework on the GPU (called GENIE), aiming to reduce the programming complexity of the GPU for parallel similarity search of different data types.

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