Search Results for author: Beibei Li

Found 15 papers, 1 papers with code

Joint Power and Coverage Control of Massive UAVs in Post-Disaster Emergency Networks: An Aggregative Game-Theoretic Learning Approach

no code implementations19 Jul 2019 Jing Wu, Qimei Chen, Hao Jiang, Haozhao Wang, Yulai Xie, Wenzheng Xu, Pan Zhou, Zichuan Xu, Lixing Chen, Beibei Li, Xiumin Wang, Dapeng Oliver Wu

In the context of fifth-generation (5G)/beyond-5G (B5G) wireless communications, post-disaster emergency networks have recently gained increasing attention and interest.

AN-GCN: An Anonymous Graph Convolutional Network Defense Against Edge-Perturbing Attack

no code implementations6 May 2020 Ao Liu, Beibei Li, Tao Li, Pan Zhou, Rui Wang

In this paper, we first generalize the formulation of edge-perturbing attacks and strictly prove the vulnerability of GCNs to such attacks in node classification tasks.

Adversarial Attack Classification +4

What Makes a Star Teacher? A Hierarchical BERT Model for Evaluating Teacher's Performance in Online Education

no code implementations3 Dec 2020 Wen Wang, Honglei Zhuang, Mi Zhou, Hanyu Liu, Beibei Li

Based on these insights, we then propose a hierarchical course BERT model to predict teachers' performance in online education.

Empowering Patients Using Smart Mobile Health Platforms: Evidence From A Randomized Field Experiment

no code implementations10 Feb 2021 Anindya Ghose, Xitong Guo, Beibei Li, Yuanyuan Dang

A comparison of mobile vs. PC version of the same app demonstrates that mobile has a stronger effect than PC in helping patients make these behavioral modifications with respect to diet, exercise and lifestyle, which leads to an improvement in their healthcare outcomes.

Improving Sequential Recommendation with Attribute-augmented Graph Neural Networks

no code implementations10 Mar 2021 Xinzhou Dong, Beihong Jin, Wei Zhuo, Beibei Li, Taofeng Xue

Many practical recommender systems provide item recommendation for different users only via mining user-item interactions but totally ignoring the rich attribute information of items that users interact with.

Attribute Sequential Recommendation

Uncovering the Source of Machine Bias

no code implementations9 Jan 2022 Xiyang Hu, Yan Huang, Beibei Li, Tian Lu

We find two types of biases in gender, preference-based bias and belief-based bias, are present in human evaluators' decisions.

counterfactual

Improving Micro-video Recommendation via Contrastive Multiple Interests

1 code implementation19 May 2022 Beibei Li, Beihong Jin, Jiageng Song, Yisong Yu, Yiyuan Zheng, Wei Zhuo

With the rapid increase of micro-video creators and viewers, how to make personalized recommendations from a large number of candidates to viewers begins to attract more and more attention.

Contrastive Learning

Improving Micro-video Recommendation by Controlling Position Bias

no code implementations9 Aug 2022 Yisong Yu, Beihong Jin, Jiageng Song, Beibei Li, Yiyuan Zheng, Wei Zhu

Although the micro-video recommendation can be naturally treated as the sequential recommendation, the previous sequential recommendation models do not fully consider the characteristics of micro-video apps, and in their inductive biases, the role of positions is not in accord with the reality in the micro-video scenario.

Contrastive Learning Position +1

FedCliP: Federated Learning with Client Pruning

no code implementations17 Jan 2023 Beibei Li, Zerui Shao, Ao Liu, Peiran Wang

The prevalent communication efficient federated learning (FL) frameworks usually take advantages of model gradient compression or model distillation.

Federated Learning

Sufficient Control of Complex Networks

no code implementations10 Mar 2023 Xiang Li, Guoqi Li, Leitao Gao, Beibei Li, Gaoxi Xiao

In this paper, we propose to study on sufficient control of complex networks which is to control a sufficiently large portion of the network, where only the quantity of controllable nodes matters.

Inclusive FinTech Lending via Contrastive Learning and Domain Adaptation

no code implementations10 May 2023 Xiyang Hu, Yan Huang, Beibei Li, Tian Lu

We use contrastive learning to train our feature extractor on unapproved (unlabeled) loan applications and use domain adaptation to generalize the performance of our label predictor.

Contrastive Learning Decision Making +1

An attention-based deep learning network for predicting Platinum resistance in ovarian cancer

no code implementations8 Nov 2023 Haoming Zhuang, Beibei Li, Jingtong Ma, Patrice Monkam, Shouliang Qi, Wei Qian, Dianning He

Multimodal data from PET/CT images of the regions of interest (ROI) were used to predict platinum resistance in patients.

Towards Inductive Robustness: Distilling and Fostering Wave-induced Resonance in Transductive GCNs Against Graph Adversarial Attacks

no code implementations14 Dec 2023 Ao Liu, Wenshan Li, Tao Li, Beibei Li, Hanyuan Huang, Pan Zhou

We then prove that merely three MP iterations within GCNs can induce signal resonance between nodes and edges, manifesting as a coupling between nodes and their distillable surrounding local subgraph.

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