Results 191 to 200 of about 93,670 (235)
Infrared image registration of damage in the aircraft skin based on lie group machine learning
The 26th Chinese Control and Decision Conference (2014 CCDC), 2014 Yunlin Luo +3 more
semanticscholar +3 more sources
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Lie group machine learning's axiom hypothesizes
2006 IEEE International Conference on Granular Computing, 2006profound inherent theory. [5] It just can meet the needs of machine learning and describe the procedure of machine learning clearly. So Lie group machine learning is formed. This paper is based on the basic conceptions of machine learning and gives the generalization hypothesis axiom; the partition independence hypothesis axiom; the duality hypothesis ...
null Huan Xu, null Fan-zhang Li
openaire +2 more sources
Souriau-Casimir Lie Groups Thermodynamics and Machine Learning
, 2021 In 1969, Jean-Marie Souriau introduced a “Lie Groups Thermodynamics” in the framework of Symplectic model of Statistical Mechanics. This Souriau’s model considers the statistical mechanics of dynamic systems in their “space of evolution” associated to a homogeneous symplectic manifold by a Lagrange 2-form, and defines in case of non-null cohomology ...
Frédéric Barbaresco
openaire +2 more sources
Implementation of Machine Learning in BCI Based Lie Detection
2022 IEEE World AI IoT Congress (AIIoT), 2022In this study, EEG, fNIRS, and HRV signals, recorded from a group of subjects when they were answering a series of true or false questions, were used to see if there is a correlation between BCI results and lying. The EEG and fNIRS signals were collected
M. Khalil +3 more
semanticscholar +1 more source
IEEE Transactions on Geoscience and Remote Sensing, 2021
The existing convolutional neural network (CNN) models have shown excellent performance in remote sensing scene classification. However, the structure of such models is becoming more and more complex, and the learning of low-level features is difficult ...
Chengjun Xu, G. Zhu, Jin Shu
semanticscholar +1 more source
The existing convolutional neural network (CNN) models have shown excellent performance in remote sensing scene classification. However, the structure of such models is becoming more and more complex, and the learning of low-level features is difficult ...
Chengjun Xu, G. Zhu, Jin Shu
semanticscholar +1 more source
Lie Group spatial attention mechanism model for remote sensing scene classification
International Journal of Remote Sensing, 2022Utilizing discriminative features to represent data samples is a significant step, and the remote sensing domain is no exception. Most existing convolutional neural network (CNN) models have achieved great results.
Chengjun Xu, G. Zhu, Jin Shu
semanticscholar +1 more source
Journal of Imaging
Remote sensing scene classification (RSSC) plays a crucial role in Earth observation. Current deep learning methods, while accurate, tend to focus on high-level semantic features and overlook complementary shallow details such as edges and textures ...
Xuefei Xu, Chenjun Xu
semanticscholar +1 more source
Remote sensing scene classification (RSSC) plays a crucial role in Earth observation. Current deep learning methods, while accurate, tend to focus on high-level semantic features and overlook complementary shallow details such as edges and textures ...
Xuefei Xu, Chenjun Xu
semanticscholar +1 more source
arXiv.org
Quantum neural networks (QNNs) and parameterized quantum circuits (PQCs) are key building blocks for near-term quantum machine learning. However, their scalability is constrained by excessive parameters, barren plateaus, and hardware limitations.
Haijian Shao +4 more
semanticscholar +1 more source
Quantum neural networks (QNNs) and parameterized quantum circuits (PQCs) are key building blocks for near-term quantum machine learning. However, their scalability is constrained by excessive parameters, barren plateaus, and hardware limitations.
Haijian Shao +4 more
semanticscholar +1 more source
A Lie Group Approach to Riemannian Batch Normalization
International Conference on Learning RepresentationsManifold-valued measurements exist in numerous applications within computer vision and machine learning. Recent studies have extended Deep Neural Networks (DNNs) to manifolds, and concomitantly, normalization techniques have also been adapted to several ...
Ziheng Chen +3 more
semanticscholar +1 more source
Lie Group-Based Optimization of the Greater Cane Rat Algorithm
2024 International Symposium on Parallel Computing and Distributed Systems (PCDS)Optimizing algorithms, due to their importance, are widely applied in fields such as engineering design, logistics management, and machine learning. However, most current models have potential shortcomings, such as the complexity of parameter tuning and ...
Xinpeng Xu, Yukai Du, Chuang Qin
semanticscholar +1 more source