Abstract: With the advancement of deep learning techniques, deep neural networks have progressively supplanted traditional machine learning methods for hyperspectral image (HSI) classification, ...

In the following example, I expected CUDA version, similar to the CPU version, to also output feature importance to be "0 600" when feature penalty is 0 on the first feature. import numpy as np import ...

Since transformer-based language models were introduced in 2017, they have been shown to be extraordinarily effective across a variety of NLP tasks including but not limited to language generation.

This study presents data on sex differences in gene expression across organs of four mice taxa. The authors have generated a unique and convincing dataset that fills a gap left by previous studies.

1 Information Statistics Center, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China 2 School of Computer Science and Technology, Hubei Business ...

Abstract: Efficient and accurate small molecule classification methods can significantly improve the efficiency of scientific research and industrial applications, but in real scenarios, many datasets ...

ONNX cannot properly save an XGBoost binary classification model when it is trained on an imbalanced dataset. When I create the dataset for the XGBoost binary classification model like this: ...

In this study, we propose a novel modularized Quantum Neural Network (mQNN) model tailored to address the binary classification problem on the MNIST dataset. The mQNN organizes input information using ...

1 School of Mathematics and Statistics, Guilin University of Technology, Guilin, China. 2 Guangxi Colleges and Universities Key Laboratory of Applied Statistics, Guilin, China. The stochastic ...