In order to solve this problem, this paper designs Multi-Branch Neural Network (MBNN), which is a new type neural network architecture that can use the unbalance of sample categories in industrial equipment fault diagnosis for fast inference.

This is the official Pytorch implementation of mBNN: "Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference. ICML 2023" by Insung Kong, Dongyoon Yang, Jongjin Lee, Ilsang Ohn, Gyuseung Baek and Yongdai Kim

In order to solve this problem, this paper designs Multi-Branch Neural Network (MBNN), which is a new type neural network architecture that can use the unbalance of sample categories in industrial equipment fault diagnosis for fast inference.

2021年1月15日 · Multi-Branch Neural Network (MBNN) is a new type neural network architecture that can use the unbalance of sample categories in industrial equipment fault diagnosis for fast inference and can greatly improve the inference speed while ensuring the recognition accuracy, especially on resource-constrained platforms.

2023年9月1日 · Specifically, we first design a novel multi-branch scalable neural network (MBSNN) architecture, which features multiple subnets to provide optional intelligent services of different accuracy. Then, we propose a stepwise incremental training technique and a learning rate dual decay strategy that can efficiently train MBSNN and increase accuracy.

2022年6月29日 · 分析一下，就模型MBNN而言，确实block的情况推理时间走的简单分支，相较common、block0.75的情况，就推理时间而言，自然就减小了许多 如何解释第二分支下的推理时间减少呢？

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2024年3月7日 · STREAM | DL — https://lnk.to/Heartbeat_Boom_Boom_Bam FOLLOW MBNN: Instagram - https://bit.ly/3Szk37e Spotify - https://spoti.fi/3UCq6tr SoundCloud - https://bit.ly/3S6YScN YouTube - @mbnnmusic VK...

In order to solve this problem, this paper designs Multi-Branch Neural Network (MBNN), which is a new type neural network architecture that can use the unbalance of sample categories in industrial equipment fault diagnosis for fast inference.

2024年11月6日 · Multiple hidden layer BNN (MBNN) has a more complex architecture than single hidden layer BNN (SBNN) and is expected to achieve better generalization performance for complex problems. But the corresponding architectural design problem is much more complex, and there is no previous experience in the literature.