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Found 19 papers, 6 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.
PeriodicLoRA: Breaking the Low-Rank Bottleneck in LoRA Optimization
Although LoRA fine-tuning is effective, there is still a performance gap compared to full fine-tuning, since its weight update is limited to low-rank matrices.
Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding
To mitigate the high inference latency stemming from autoregressive decoding in Large Language Models (LLMs), Speculative Decoding has emerged as a novel decoding paradigm for LLM inference.
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.
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.
On the Opportunities of Green Computing: A Survey
To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
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.
Not All Demonstration Examples are Equally Beneficial: Reweighting Demonstration Examples for In-Context Learning
To assess the quality of weights in the absence of additional validation data, we design a masked self-prediction (MSP) score that exhibits a strong correlation with the final ICL performance.
Low-resource Neural Machine Translation with Cross-modal Alignment
How to achieve neural machine translation with limited parallel data?
Contrastive Learning
Low-Resource Neural Machine Translation
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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.
A Structure Feature Algorithm for Multi-modal Forearm Registration
Augmented reality technology based on image registration is becoming increasingly popular for the convenience of pre-surgery preparation and medical education.
LUAI Challenge 2021 on Learning to Understand Aerial Images
This report summarizes the results of Learning to Understand Aerial Images (LUAI) 2021 challenge held on ICCV 2021, which focuses on object detection and semantic segmentation in aerial images.
CrossRoI: Cross-camera Region of Interest Optimization for Efficient Real Time Video Analytics at Scale
Video cameras are pervasively deployed in city scale for public good or community safety (i. e. traffic monitoring or suspected person tracking).
DeepRT: A Soft Real Time Scheduler for Computer Vision Applications on the Edge
Based on this analysis, we propose a GPU scheduler, DeepRT, which provides latency guarantee to the requests while maintaining high overall system throughput.
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 Joint Model for Aspect-Category Sentiment Analysis with Shared Sentiment Prediction Layer
Aspect-category sentiment analysis (ACSA) aims to predict the aspect categories mentioned in texts and their corresponding sentiment polarities.
CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation
In this research, we propose CAMEL, a weakly supervised learning framework for histopathology image segmentation using only image-level labels.
BAGS: An automatic homework grading system using the pictures taken by smart phones
To solve these problems, we propose BAGS, an automatic homework grading system which can effectively locate and recognize handwritten answers.
Depth Acquisition from Density Modulated Binary Patterns
First, we propose an algorithm to design the patterns to carry more phase information without compromising the depth reconstruction from a single captured image as with Kinect.
