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Found 8 papers, 3 papers with code
Data-Centric Evolution in Autonomous Driving: A Comprehensive Survey of Big Data System, Data Mining, and Closed-Loop Technologies
There has been a huge bottleneck regarding the upper bound of autonomous driving algorithm performance, a consensus from academia and industry believes that the key to surmount the bottleneck lies in data-centric autonomous driving technology.
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.
SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma
The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly impacting patient prognosis.
STS-CCL: Spatial-Temporal Synchronous Contextual Contrastive Learning for Urban Traffic Forecasting
Efficiently capturing the complex spatiotemporal representations from large-scale unlabeled traffic data remains to be a challenging task.
Few-shot learning with improved local representations via bias rectify module
Recent approaches based on metric learning have achieved great progress in few-shot learning.
Tao: A Learning Framework for Adaptive Nearest Neighbor Search using Static Features Only
In the middle of query execution, AdaptNN collects a number of runtime features and predicts termination condition for each individual query, by which better end-to-end latency is attained.
A Note on Graph-Based Nearest Neighbor Search
And which data property affects the efficiency and how?
