Self-supervised learning for time series analysis: Taxonomy, progress, and prospects
Self-supervised learning (SSL) has recently achieved impressive performance on various
time series tasks. The most prominent advantage of SSL is that it reduces the dependence …
time series tasks. The most prominent advantage of SSL is that it reduces the dependence …
A survey on time-series pre-trained models
Time-Series Mining (TSM) is an important research area since it shows great potential in
practical applications. Deep learning models that rely on massive labeled data have been …
practical applications. Deep learning models that rely on massive labeled data have been …
When deep learning-based soft sensors encounter reliability challenges: a practical knowledge-guided adversarial attack and its defense
Deep learning-based soft sensors (DLSSs) have been demonstrated to exhibit significantly
improved sensing accuracy; however, their vulnerability to adversarial attacks affects their …
improved sensing accuracy; however, their vulnerability to adversarial attacks affects their …
Backtime: Backdoor attacks on multivariate time series forecasting
Abstract Multivariate Time Series (MTS) forecasting is a fundamental task with numerous
real-world applications, such as transportation, climate, and epidemiology. While a myriad of …
real-world applications, such as transportation, climate, and epidemiology. While a myriad of …
Similarity-based integrity protection for deep learning systems
Deep learning technologies have achieved remarkable success in various tasks, ranging
from computer vision, object detection to natural language processing. Unfortunately, state …
from computer vision, object detection to natural language processing. Unfortunately, state …
Towards robust learning with noisy and pseudo labels for text classification
Abstract Unlike Positive Training (PT), Negative Training (NT) is an indirect learning
technique that trains the model on a combination of clean and noisy data using …
technique that trains the model on a combination of clean and noisy data using …
AGS: Transferable adversarial attack for person re-identification by adaptive gradient similarity attack
Person re-identification (Re-ID) has achieved tremendous success in the fields of computer
vision and security. However, Re-ID models are susceptible to adversarial examples, which …
vision and security. However, Re-ID models are susceptible to adversarial examples, which …
3d adversarial attacks beyond point cloud
Recently, 3D deep learning models have been shown to be susceptible to adversarial
attacks like their 2D counterparts. Most of the state-of-the-art (SOTA) 3D adversarial attacks …
attacks like their 2D counterparts. Most of the state-of-the-art (SOTA) 3D adversarial attacks …
Economic system forecasting based on temporal fusion transformers: Multi-dimensional evaluation and cross-model comparative analysis
Y Han, Y Tian, L Yu, Y Gao - Neurocomputing, 2023 - Elsevier
Although helpful in reducing the uncertainty associated with economic activities, economic
forecasting often suffers from low accuracy. Recognizing the high compatibility between …
forecasting often suffers from low accuracy. Recognizing the high compatibility between …
ERGCN: Data enhancement-based robust graph convolutional network against adversarial attacks
With recent advancements, graph neural networks (GNNs) have shown considerable
potential for various graph-related tasks, and their applications have gained considerable …
potential for various graph-related tasks, and their applications have gained considerable …