A Randomized Block-Coordinate Adam online learning optimization algorithm

Zhou, Yangfan; Zhang, Mingchuan; Zhu, Junlong; Zheng, Ruijuan; Wu, Qingtao

doi:10.1007/s00521-020-04718-9

Cited by 27 publications

(12 citation statements)

References 22 publications

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“…The loss function is cross-entropy loss function and using the Adam optimizer [ 44 ]. In the training set, there were approximately 20 times more moso bamboo OG than moso bamboo non-OG, which led to an imbalance problem during training.…”

Section: Methodsmentioning

confidence: 99%

A deep learning approach for orphan gene identification in moso bamboo (Phyllostachys edulis) based on the CNN + Transformer model

et al. 2022

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Background Orphan gene play an important role in the environmental stresses of many species and their identification is a critical step to understand biological functions. Moso bamboo has high ecological, economic and cultural value. Studies have shown that the growth of moso bamboo is influenced by various stresses. Several traditional methods are time-consuming and inefficient. Hence, the development of efficient and high-accuracy computational methods for predicting orphan genes is of great significance. Results In this paper, we propose a novel deep learning model (CNN + Transformer) for identifying orphan genes in moso bamboo. It uses a convolutional neural network in combination with a transformer neural network to capture k-mer amino acids and features between k-mer amino acids in protein sequences. The experimental results show that the average balance accuracy value of CNN + Transformer on moso bamboo dataset can reach 0.875, and the average Matthews Correlation Coefficient (MCC) value can reach 0.471. For the same testing set, the Balance Accuracy (BA), Geometric Mean (GM), Bookmaker Informedness (BM), and MCC values of the recurrent neural network, long short-term memory, gated recurrent unit, and transformer models are all lower than those of CNN + Transformer, which indicated that the model has the extensive ability for OG identification in moso bamboo. Conclusions CNN + Transformer model is feasible and obtains the credible predictive results. It may also provide valuable references for other related research. As our knowledge, this is the first model to adopt the deep learning techniques for identifying orphan genes in plants.

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Section: Methodsmentioning

confidence: 99%

A deep learning approach for orphan gene identification in moso bamboo (Phyllostachys edulis) based on the CNN + Transformer model

et al. 2022

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“…In this learning model, the optimizer chooses the Adam optimizer [15,16]. The training batch size was set to 30.…”

Section: Trainingmentioning

confidence: 99%

HER2-ResNet: A HER2 classification method based on deep residual network

Wang

Shao

Wen-sheng

et al. 2022

THC

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BACKGROUND: HER2 gene expression is one of the main reference indicators for breast cancer detection and treatment, and it is also an important target for tumor targeted therapy drug selection. Therefore, the correct detection and evaluation of HER2 gene expression has important value for clinical treatment of breast cancer. OBJECTIVE: The study goal is to better classify HER2 images. METHODS: For general convolution neural network, with the increase of network layers, over fitting phenomenon is often very serious, which requires setting the value of random descent ratio, and parameter adjustment is often time-consuming and laborious, so this paper uses residual network, with the increase of network layer, the accuracy will not be reduced. RESULTS: In this paper, a HER2 image classification algorithm based on improved residual network is proposed. Experimental results show that the proposed HER2 network has high accuracy in breast cancer assessment. Conclusion: Taking HER2 images in Stanford University database as experimental data, the accuracy of HER2 image automatic classification is improved through experiments. This method will help to reduce the detection intensity and improve the accuracy of HER2 image classification.

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“…8. Finally, we use an adaptive moment estimation (Adam) algorithm [30] to optimize the loss function () L  . It should be noted that the estimated noise level  is related to the predicted value ˆi y .…”

Section: A Multi-scale Densely Connected Noise Estimation Modulementioning

confidence: 99%

Seismic Random Noise Removal Based on a Multiscale Convolution and Densely Connected Network for Noise Level Evaluation

Luo

Guo

et al. 2022

IEEE Access

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Traditional denoising methods for seismic exploration data design a corresponding mathematical denoising model batch according to the different properties of different random noises, which is a tedious and time-consuming process. To solve this problem, this paper proposes a deep convolutional neural network denoising model based on noise estimation (MCD-DCNN). This model is primarily composed of two modules, the noise estimation module and the denoising module. The noise estimation module uses a multiscale convolutional neural network to better extract the characteristics of random noise in the seismic data. To make full use of the extracted features, a dense connection method is adopted between the multiscale convolutions in the noise estimation module. In the denoising module, we use multiscale convolutions and dense connections to replace the original convolutional neural network and use the residual structure (ResNet) and batch normalization (BN) to improve the denoising effect and running speed of the model. In this experiment, single trace and simple and complex profile data are used as input to simulate the real data processing environment. Finally, we compare the denoising effects of the MCD-DCNN model proposed in this paper with the current mainstream feed-forward denoising convolutional neural network (DnCNN) and a fast and flexible denoising convolutional neural network (FFDNet) models. The comprehensive results show that under the condition of a given prior noise level, the denoising performance of the FFDNet and MCD-DCNN models are comparable. In the absence of a priori noise level, the denoising performance of the FFDNet model drops sharply, while the denoising performance of MCD-DCNN is not affected; therefore, MCD-DCNN is more in line with actual seismic denoising.

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A Randomized Block-Coordinate Adam online learning optimization algorithm

Cited by 27 publications

References 22 publications

A deep learning approach for orphan gene identification in moso bamboo (Phyllostachys edulis) based on the CNN + Transformer model

A deep learning approach for orphan gene identification in moso bamboo (Phyllostachys edulis) based on the CNN + Transformer model

HER2-ResNet: A HER2 classification method based on deep residual network

Seismic Random Noise Removal Based on a Multiscale Convolution and Densely Connected Network for Noise Level Evaluation

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