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Found 20 papers, 6 papers with code
Cost-Effective Label-free Node Classification with LLMs
Graph neural networks (GNNs) have emerged as go-to models for node classification in graph data due to their powerful abilities in fusing graph structures and attributes.
Multi-objective Optimization in CPU Design Space Exploration: Attention is All You Need
This approach enhances both the prediction accuracy and interpretability of the performance model.
SiHGNN: Leveraging Properties of Semantic Graphs for Efficient HGNN Acceleration
In this work, we focus on leveraging the properties of semantic graphs to enhance HGNN performance.
Characterizing and Understanding HGNN Training on GPUs
Owing to their remarkable representation capabilities for heterogeneous graph data, Heterogeneous Graph Neural Networks (HGNNs) have been widely adopted in many critical real-world domains such as recommendation systems and medical analysis.
ITERTL: An Iterative Framework for Fine-tuning LLMs for RTL Code Generation
However, the existing approaches to fine-tune LLMs on RTL codes typically are conducted on fixed datasets, which do not fully stimulate the capability of LLMs and require large amounts of reference data.
Disttack: Graph Adversarial Attacks Toward Distributed GNN Training
In this study, we introduce Disttack, the first framework of adversarial attacks for distributed GNN training that leverages the characteristics of frequent gradient updates in a distributed system.
Revisiting Edge Perturbation for Graph Neural Network in Graph Data Augmentation and Attack
It can be categorized into two veins based on their effects on the performance of graph neural networks (GNNs), i. e., graph data augmentation and attack.
A Comprehensive Survey on Distributed Training of Graph Neural Networks
This situation poses a considerable challenge for newcomers, hindering their ability to grasp a comprehensive understanding of the workflows, computational patterns, communication strategies, and optimization techniques employed in distributed GNN training.
Rethinking Efficiency and Redundancy in Training Large-scale Graphs
Thereby, we propose to drop redundancy and improve efficiency of training large-scale graphs with GNNs, by rethinking the inherent characteristics in a graph.
Simple and Efficient Heterogeneous Graph Neural Network
Heterogeneous graph neural networks (HGNNs) have powerful capability to embed rich structural and semantic information of a heterogeneous graph into node representations.
Ranked #1 on
Heterogeneous Node Classification
on Freebase (Heterogeneous Node Classification)
(Macro-F1 metric)
Characterizing and Understanding Distributed GNN Training on GPUs
Graph neural network (GNN) has been demonstrated to be a powerful model in many domains for its effectiveness in learning over graphs.
Survey on Graph Neural Network Acceleration: An Algorithmic Perspective
Next, we provide comparisons from aspects of the efficiency and characteristics of these methods.
GNNSampler: Bridging the Gap between Sampling Algorithms of GNN and Hardware
Extensive experiments show that our method is universal to mainstream sampling algorithms and helps significantly reduce the training time, especially in large-scale graphs.
Tackling Variabilities in Autonomous Driving
In this work, we aim to extensively explore the above system design challenges and these challenges motivate us to propose a comprehensive framework that synergistically handles the heterogeneous hardware accelerator design principles, system design criteria, and task scheduling mechanism.
Sampling methods for efficient training of graph convolutional networks: A survey
Graph Convolutional Networks (GCNs) have received significant attention from various research fields due to the excellent performance in learning graph representations.
Video Face Recognition System: RetinaFace-mnet-faster and Secondary Search
Ours is suitable for large-scale datasets, and experimental results show that our method is 82% faster than the violent retrieval for the single-frame detection.
Top-Related Meta-Learning Method for Few-Shot Object Detection
Therefore, based on semantic features, we propose a Top-C classification loss (i. e., TCL-C) for classification task and a category-based grouping mechanism for category-based meta-features obtained by the meta-model.
HyGCN: A GCN Accelerator with Hybrid Architecture
In this work, we first characterize the hybrid execution patterns of GCNs on Intel Xeon CPU.
Distributed, Parallel, and Cluster Computing
Pixel-Semantic Revise of Position Learning A One-Stage Object Detector with A Shared Encoder-Decoder
They cannot detect objects by semantic features, adaptively.
Utilizing the Instability in Weakly Supervised Object Detection
Weakly supervised object detection (WSOD) focuses on training object detector with only image-level annotations, and is challenging due to the gap between the supervision and the objective.
