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Bing Hu

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3 papers

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NeurIPS Conference 2025 Conference Paper

FlowMoE: A Scalable Pipeline Scheduling Framework for Distributed Mixture-of-Experts Training

Yunqi Gao
Bing Hu
Boloursaz Mashhadi
A-Long Jin
Yanfeng Zhang
Pei Xiao
Rahim Tafazolli
Merouane DEBBAH

The parameter size of modern large language models (LLMs) can be scaled up to the trillion-level via the sparsely-activated Mixture-of-Experts (MoE) technique to avoid excessive increase of the computational costs. To further improve training efficiency, pipelining computation and communication has become a promising solution for distributed MoE training. However, existing work primarily focuses on scheduling tasks within the MoE layer, such as expert computing and all-to-all (A2A) communication, while neglecting other key operations including multi-head attention (MHA) computing, gating, and all-reduce communication. In this paper, we propose FlowMoE, a scalable framework for scheduling multi-type task pipelines. First, FlowMoE constructs a unified pipeline to consistently scheduling MHA computing, gating, expert computing, and A2A communication. Second, FlowMoE introduces a tensor chunk-based priority scheduling mechanism to overlap the all-reduce communication with all computing tasks. We implement FlowMoE as an adaptive and generic framework atop PyTorch. Extensive experiments with 675 typical MoE layers and four real-world MoE models across two GPU clusters demonstrate that our proposed FlowMoE framework outperforms state-of-the-art MoE training frameworks, reducing training time by14%-57%, energy consumption by 10%-39%, and memory usage by 7%-32%. FlowMoE’s code is anonymously available at https: //anonymous. 4open. science/r/FlowMoE.

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EAAI Journal 2025 Journal Article

Unsupervised motion-based anomaly detection with graph attention networks for industrial robots labeling

Jinrui Han
Zhen Chen
Di Zhou
Bing Hu
Tangbin Xia
Ershun Pan

As automated labeling on products in intelligent manufacturing grows in importance, detecting anomalies in the end-effectors used for industrial robots labeling is essential for maintaining production line stability and efficiency. Considering the distinct characteristics of specific movements in the labeling process, different motions, such as moving, labeling and rolling, contribute different effects to end-effector abnormalities. It's challenging to distinguish between normal and anomalies, instead of treating all motions as a homogeneous whole. Also, real-world industrial scenarios often lack the sufficient data on abnormal states, and resource constraints limit computation efficiency. In view of this, this paper aims to develop a task-specific anomaly detection solution tailored to the distinct motions of industrial robots labeling. To achieve this goal, an unsupervised, motion-based anomaly detection framework is proposed. The raw sensor signals from each motion are segmented and a group of encoder networks are employed to extract latent representations for each motion. Then, these motions are modeled as nodes in a graph, where a feature fusion module based on a Graph Attention Network (GAT) captures the interrelationships between them. A memory-augmented reconstruction module with multi-scale skip connections enhances the model's ability to detect anomalies. Finally, an anomaly detection module identifies abnormal states of the end-effector. Experimental validations are conducted on a dataset from real-world steel coil labeling task. The results show that the proposed framework can achieve an average performance of 98. 24% with an inference time of 15 ms, also demonstrating the effectiveness of its structural design and key modules.

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AAAI Conference 2024 Conference Paper

Deep Variational Incomplete Multi-View Clustering: Exploring Shared Clustering Structures

Gehui Xu
Jie Wen
Chengliang Liu
Bing Hu
Yicheng Liu
Lunke Fei
Wei Wang

Incomplete multi-view clustering (IMVC) aims to reveal shared clustering structures within multi-view data, where only partial views of the samples are available. Existing IMVC methods primarily suffer from two issues: 1) Imputation-based methods inevitably introduce inaccurate imputations, which in turn degrade clustering performance; 2) Imputation-free methods are susceptible to unbalanced information among views and fail to fully exploit shared information. To address these issues, we propose a novel method based on variational autoencoders. Specifically, we adopt multiple view-specific encoders to extract information from each view and utilize the Product-of-Experts approach to efficiently aggregate information to obtain the common representation. To enhance the shared information in the common representation, we introduce a coherence objective to mitigate the influence of information imbalance. By incorporating the Mixture-of-Gaussians prior information into the latent representation, our proposed method is able to learn the common representation with clustering-friendly structures. Extensive experiments on four datasets show that our method achieves competitive clustering performance compared with state-of-the-art methods.

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