Search Results for author:
Found 8 papers, 2 papers with code
Multi-scale 2D Representation Learning for weakly-supervised moment retrieval
Specifically, we first construct a two-dimensional map for each temporal scale to capture the temporal dependencies between candidates.
DistFL: Distribution-aware Federated Learning for Mobile Scenarios
Previous studies focus on the "symptoms" directly, as they try to improve the accuracy or detect possible attacks by adding extra steps to conventional FL models.
Learning Heatmap-Style Jigsaw Puzzles Provides Good Pretraining for 2D Human Pose Estimation
We note that 2D pose estimation task is highly dependent on the contextual relationship between image patches, thus we introduce a self-supervised method for pretraining 2D pose estimation networks.
SIGL: Securing Software Installations Through Deep Graph Learning
We introduce SIGL, a new tool for detecting malicious behavior during software installation.
Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs
Detecting anomalies in dynamic graphs is a vital task, with numerous practical applications in areas such as security, finance, and social media.
Heterogeneous Graph Matching Networks
Information systems have widely been the target of malware attacks.
Attentional Heterogeneous Graph Neural Network: Application to Program Reidentification
The key idea is to leverage the representation learning of the heterogeneous program behavior graph to guide the reidentification process.
SAQL: A Stream-based Query System for Real-Time Abnormal System Behavior Detection
To facilitate the task of expressing anomalies based on expert knowledge, our system provides a domain-specific query language, SAQL, which allows analysts to express models for (1) rule-based anomalies, (2) time-series anomalies, (3) invariant-based anomalies, and (4) outlier-based anomalies.
Cryptography and Security Databases
