Search Results for author:
Found 18 papers, 3 papers with code
GLADformer: A Mixed Perspective for Graph-level Anomaly Detection
However, current methods are constrained by their receptive fields, struggling to learn global features within the graphs.
E2USD: Efficient-yet-effective Unsupervised State Detection for Multivariate Time Series
We propose E2Usd that enables efficient-yet-accurate unsupervised MTS state detection.
Disentangling Imperfect: A Wavelet-Infused Multilevel Heterogeneous Network for Human Activity Recognition in Flawed Wearable Sensor Data
To address these challenges, we propose a multilevel heterogeneous neural network, called MHNN, for sensor data analysis.
TBDetector:Transformer-Based Detector for Advanced Persistent Threats with Provenance Graph
APT detection is difficult to detect due to the long-term latency, covert and slow multistage attack patterns of Advanced Persistent Threat (APT).
LightCTS: A Lightweight Framework for Correlated Time Series Forecasting
Many deep learning models have been proposed to improve the accuracy of CTS forecasting.
Ranked #1 on
Traffic Prediction
on PeMS04
(FLOPs(M) metric, using extra
training data)
Computational Efficiency
Correlated Time Series Forecasting
+4
AutoPINN: When AutoML Meets Physics-Informed Neural Networks
We then propose a resource-aware search strategy to explore the search space to find the best PINN model under different resource constraints.
Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting
To overcome these limitations, we propose SEARCH, a joint, scalable framework, to automatically devise effective CTS forecasting models.
Design Automation for Fast, Lightweight, and Effective Deep Learning Models: A Survey
A key challenge is that while the application of deep models often incurs substantial memory and computational costs, edge devices typically offer only very limited storage and computational capabilities that may vary substantially across devices.
Uncertainty Quantification for Traffic Forecasting: A Unified Approach
To achieve this, we develop Deep Spatio-Temporal Uncertainty Quantification (DeepSTUQ), which can estimate both aleatoric and epistemic uncertainty.
AutoCTS: Automated Correlated Time Series Forecasting -- Extended Version
Specifically, we design both a micro and a macro search space to model possible architectures of ST-blocks and the connections among heterogeneous ST-blocks, and we provide a search strategy that is able to jointly explore the search spaces to identify optimal forecasting models.
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
In this study, we present a survey of the state-of-the-art deep learning methods for sensor-based human activity recognition.
Multi-agent Attentional Activity Recognition
And the multiple agents in the proposed model represent activities with collective motions across body parts by independently selecting modalities associated with single motions.
Distributionally Robust Semi-Supervised Learning for People-Centric Sensing
Semi-supervised learning is crucial for alleviating labelling burdens in people-centric sensing.
Interpretable Parallel Recurrent Neural Networks with Convolutional Attentions for Multi-Modality Activity Modeling
Multimodal features play a key role in wearable sensor-based human activity recognition (HAR).
Fullie and Wiselie: A Dual-Stream Recurrent Convolutional Attention Model for Activity Recognition
Multimodal features play a key role in wearable sensor based Human Activity Recognition (HAR).
Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis
In this paper, we attempt to solve the above challenges by proposing an approach which has better EEG interpretation ability via raw Electroencephalography (EEG) signal analysis for multi-person and multi-class brain activity recognition.
Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals
An electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the outside world by interpreting the EEG signals of their brains to interact with devices such as wheelchairs and intelligent robots.
Cascade and Parallel Convolutional Recurrent Neural Networks on EEG-based Intention Recognition for Brain Computer Interface
Brain-Computer Interface (BCI) is a system empowering humans to communicate with or control the outside world with exclusively brain intentions.
Human-Computer Interaction Neurons and Cognition
