Search Results for author:
Found 10 papers, 2 papers with code
Seismic First Break Picking in a Higher Dimension Using Deep Graph Learning
Addressing this, we propose a novel approach using deep graph learning called DGL-FB, constructing a large graph to efficiently extract information.
MPAT: Building Robust Deep Neural Networks against Textual Adversarial Attacks
Deep neural networks have been proven to be vulnerable to adversarial examples and various methods have been proposed to defend against adversarial attacks for natural language processing tasks.
Designs and Implementations in Neural Network-based Video Coding
The past decade has witnessed the huge success of deep learning in well-known artificial intelligence applications such as face recognition, autonomous driving, and large language model like ChatGPT.
Seismic Data Interpolation based on Denoising Diffusion Implicit Models with Resampling
The model training is established on the denoising diffusion probabilistic model, where U-Net is equipped with the multi-head self-attention to match the noise in each step.
UPNet: Uncertainty-based Picking Deep Learning Network for Robust First Break Picking
Many deep neural networks (DNNs)-based automatic picking methods have been proposed to accelerate this processing.
Federated Coordinate Descent for Privacy-Preserving Multiparty Linear Regression
We show that the FCD scheme fills the gap of multiparty secure Coordinate Descent methods and is applicable for general linear regressions, including linear, ridge and lasso regressions.
MSSPN: Automatic First Arrival Picking using Multi-Stage Segmentation Picking Network
Besides, since the gather data is a set of signals which are greatly different from the natural images, it is difficult for the current method to solve the FAT picking problem in case of a low Signal to Noise Ratio (SNR).
Learning Free Gait Transition for Quadruped Robots via Phase-Guided Controller
Reinforcement learning has become a powerful tool to formulate controllers for legged robots.
Linking Attention-Based Multiscale CNN With Dynamical GCN for Driving Fatigue Detection
Finally, the critical brain regions and connections for driving fatigue detection were investigated through the dynamically learned adjacency matrix. Index Terms— Attention-based multiscale convolutional neural network (CNN), driving fatigue, dynamical graph convolution network (GCN), electroencephalography (EEG), spatiotemporalstructure.
Defense of Word-level Adversarial Attacks via Random Substitution Encoding
The adversarial attacks against deep neural networks on computer vision tasks have spawned many new technologies that help protect models from avoiding false predictions.
