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Found 8 papers, 5 papers with code
Dual-Personalizing Adapter for Federated Foundation Models
To address challenges in this new setting, we explore a simple yet effective solution to learn a comprehensive foundation model.
PatchAD: Patch-based MLP-Mixer for Time Series Anomaly Detection
In this study, we introduce PatchAD, a novel multi-scale patch-based MLP-Mixer architecture that leverages contrastive learning for representational extraction and anomaly detection.
AI-Based Energy Transportation Safety: Pipeline Radial Threat Estimation Using Intelligent Sensing System
This refinement is crucial in effectively identifying genuine threats to pipelines, thus enhancing the safety of energy transportation.
DynPoint: Dynamic Neural Point For View Synthesis
The introduction of neural radiance fields has greatly improved the effectiveness of view synthesis for monocular videos.
DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection
On the other hand, contrastive learning aims to find a representation that can clearly distinguish any instance from the others, which can bring a more natural and promising representation for time series anomaly detection.
SGDP: A Stream-Graph Neural Network Based Data Prefetcher
This paper proposes a novel Stream-Graph neural network-based Data Prefetcher (SGDP).
Do Time Constraints Re-Prioritize Attention to Shapes During Visual Photo Inspection?
People's visual experiences of the world are easy to carve up and examine along natural language boundaries, e. g., by category labels, attribute labels, etc.
Internal-transfer Weighting of Multi-task Learning for Lung Cancer Detection
To improve performance on the primary task, we propose an Internal-Transfer Weighting (ITW) strategy to suppress the loss functions on auxiliary tasks for the final stages of training.
