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

2024年7月21日 · For this purpose, here we introduce an atomic attention mechanism into many-body function corrected neural network (MBNN), namely, MBNN-att ML model, to predict both the extensive and intensive properties of molecules and materials. The MBNN-att uses explicit function descriptors as the inputs for the atom-based feed-forward neural network (NN).

我想检测小目标，奈何小目标召回率、准确率都不高，所以想添加一个小目标检测头用于改善小目标效果。 其实官方给的有带 p2检测头 的配置文件，之前有训练过，小目标确实有提升，但是检测速度也有下降。 我的目标只有小目标，所以可以去掉一个p5检测头（用于检测大目标的）。 二、对比配置文件. 首先我们来看一下官方的 yolov8 -p2.yaml配置文件,在 ultralytics /models/v8目录下面。 (ps:我的yolov8版本是8.0.93) 骨干网络、neck部分，每行代表一层网络结构。 head部分 …

2023年11月7日 · yolov8改进了bifpn并融合p2小目标检测层，提高了目标检测精度。 BiFPN是双向特征金字塔网络，用于目标检测和语义分割。 YOLOv8改进之更换BiFPN并融合P2小目标检测层

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？

Herein, we propose a low-cost neural-network-based MLP architecture for fitting global potential energy surface data, namely, G-MBNN, that can offer improved energy and force resolution on a complex potential energy surface.

Many-Body Function Neural Network with Atomic Attention Mechanism (MBNN-att) for Predicting Molecular Properties Python 4

Therefore, here we propose a modular BNN (mBNN)-based neuro-robotic system via local chemical stimulation and calcium recording. In this system, reliable evoked sensory-motor mapping (success rate $> $ 89%) from the sensory to the motor area in the mBNN was demonstrated. It is achieved in the mBNNs by combining global chemical modulation (for ...

Multi-branch Neural Networks for Anomaly Detection under adverse lighting and weather conditions - lyn1874/mbnn_ad

The feasibility of MBNN has been verified on motor bearing fault diagnosis and gearbox fault diagnosis, and its performance has been evaluated on multiple computing platforms. The results show that MBNN can greatly improve the inference speed while ensuring the recognition accuracy, especially on resource-constrained platforms.