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Found 12 papers, 3 papers with code
TimeGPT in Load Forecasting: A Large Time Series Model Perspective
Machine learning models have made significant progress in load forecasting, but their forecast accuracy is limited in cases where historical load data is scarce.
Grounding and Enhancing Grid-based Models for Neural Fields
Many contemporary studies utilize grid-based models for neural field representation, but a systematic analysis of grid-based models is still missing, hindering the improvement of those models.
AMP: Autoregressive Motion Prediction Revisited with Next Token Prediction for Autonomous Driving
As an essential task in autonomous driving (AD), motion prediction aims to predict the future states of surround objects for navigation.
ActiveAD: Planning-Oriented Active Learning for End-to-End Autonomous Driving
End-to-end differentiable learning for autonomous driving (AD) has recently become a prominent paradigm.
Improving the Accuracy and Interpretability of Neural Networks for Wind Power Forecasting
Deep neural networks (DNNs) are receiving increasing attention in wind power forecasting due to their ability to effectively capture complex patterns in wind data.
MWSIS: Multimodal Weakly Supervised Instance Segmentation with 2D Box Annotations for Autonomous Driving
Instance segmentation is a fundamental research in computer vision, especially in autonomous driving.
An Explainable Framework for Machine learning-Based Reactive Power Optimization of Distribution Network
To reduce the heavy computational burden of reactive power optimization of distribution networks, machine learning models are receiving increasing attention.
Explainable Modeling for Wind Power Forecasting: A Glass-Box Approach with High Accuracy
Machine learning models (e. g., neural networks) achieve high accuracy in wind power forecasting, but they are usually regarded as black boxes that lack interpretability.
Short-Term Power Prediction for Renewable Energy Using Hybrid Graph Convolutional Network and Long Short-Term Memory Approach
Accurate short-term solar and wind power predictions play an important role in the planning and operation of power systems.
Scenario Generation for Cooling, Heating, and Power Loads Using Generative Moment Matching Networks
Scenario generations of cooling, heating, and power loads are of great significance for the economic operation and stability analysis of integrated energy systems.
A Review of Graph Neural Networks and Their Applications in Power Systems
The data in these tasks is typically represented in Euclidean domains.
SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing
Instance-level denoising on the feature map is performed to enhance the detection to small and cluttered objects.
Ranked #33 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
