Network traffic classification is crucial for network security and network management and is one of the most important network tasks. Current state-of-the-art traffic classifiers are based on deep learning models to a...
详细信息
Depthwise convolutions are widely used in lightweight convolutional neural networks (CNNs). The performance of depthwise convolutions is mainly bounded by the memory access rather than the arithmetic operations for cl...
详细信息
Continual Learning (CL) studies the problem of developing a robust model that can learn new tasks while retaining previously learned knowledge. However, the current CL methods exclusively focus on data with annotation...
Reinforcement learning(RL)has become a dominant decision-making paradigm and has achieved notable success in many real-world ***,deep neural networks play a crucial role in unlocking RL’s potential in large-scale dec...
详细信息
Reinforcement learning(RL)has become a dominant decision-making paradigm and has achieved notable success in many real-world ***,deep neural networks play a crucial role in unlocking RL’s potential in large-scale decision-making *** by current major success of Transformer in natural language processing and computer vision,numerous bottlenecks have been overcome by combining Transformer with RL for *** paper presents a multiangle systematic survey of various Transformer-based RL(TransRL)models applied in decision-making tasks,including basic models,advanced algorithms,representative implementation instances,typical applications,and known *** work aims to provide insights into problems that inherently arise with the current RL approaches,and examines how we can address them with better TransRL *** our knowledge,we are the first to present a comprehensive review of the recent Transformer research developments in RL for *** hope that this survey provides a comprehensive review of TransRL models and inspires the RL community in its pursuit of future *** keep track of the rapid TransRL developments in the decision-making domains,we summarize the latest papers and their open-source implementations at https://***/williamyuanv0/Transformer-in-Reinforcement-Learning-for-Decision-Making-A-Survey.
Mesh generation plays a crucial role in scientific computing. Traditional mesh generation methods, such as TFI and PDE-based methods, often struggle to achieve a balance between efficiency and mesh quality. To address...
详细信息
The matrix multiplication-based convolutional algorithm, which can efficiently implement convolutions with different parameters, is the first choice of convolution performance optimization for a given chip. Based on t...
详细信息
As a classic semi-supervised approach, the Transductive Support Vector Machine (TSVM) has exhibited remarkable accuracy by utilizing unlabeled data. However, the robustness of TSVM against adversarial attacks remains ...
详细信息
ISBN:
(数字)9798350359312
ISBN:
(纸本)9798350359329
As a classic semi-supervised approach, the Transductive Support Vector Machine (TSVM) has exhibited remarkable accuracy by utilizing unlabeled data. However, the robustness of TSVM against adversarial attacks remains a subject of investigation, prompting concerns about its reliability in security-critical applications. To unveil the vulnerability of TSVM, we introduce a finite-attack model specifically tailored to its characteristics, effectively manipulating its outputs. Additionally, we present Adversarial Defense-based TSVM (AD-TSVM), the first dedicated defense scheme designed for TSVM. AD-TSVM incorporates adversarial information into the optimization process, enhancing robustness by rebuilding a customized loss function and decision margin to counteract attacks. Rigorous experiments conducted on benchmark datasets demonstrate the effectiveness of AD-TSVM in significantly improving both the accuracy and stability of TSVM when confronted with adversarial attacks. This pioneering research assesses the weaknesses of TSVM and, more importantly, offers valuable insights and solutions for developing secure and trustworthy TSVM systems in the face of emerging threats.
Accurate prediction of gene regulation rules is important for understanding complex life processes. Existing computational algorithms designed for bulk transcriptome datasets typically require a large number of time p...
详细信息
ISBN:
(纸本)9781450396868
Accurate prediction of gene regulation rules is important for understanding complex life processes. Existing computational algorithms designed for bulk transcriptome datasets typically require a large number of time points to infer gene regulatory networks (GRNs), are suitable for a small number of genes, and cannot efficiently detect potential regulatory relationships. We propose an approach based on a deep learning framework to reconstruct GRNs from bulk transcriptome datasets, assuming that the expression levels of transcription factors involved in gene regulation are strong predictors of the expression of their target genes. The algorithm uses multilayer perceptrons to infer the regulatory relationship between multiple transcription factors and a gene, and uses genetic algorithms to search for the best regulatory gene combination. The results show that our approach is more accurate than other methods for reconstructing gene regulatory networks on real-world and simulated bulk transcriptome gene expression datasets.
In airfoil numerical simulation, the mesh quality has an important influence on the accuracy and error of numerical simulation. The existing mesh quality evaluation requires a lot of manual interaction, which greatly ...
详细信息
暂无评论