GitHub

GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended Tasks

Pre-training Graph Model Phase. In the pre-training phase, we employ link prediction as the self-supervised task for pre-training the graph model. Producer Phase. In the Producer phase, we employ LLM ...
Frontiers

DWGCN: distance-weighted graph convolutional network for robust spatial domain identification in spatial transcriptomics

Graph Convolutional Networks (GCNs) are widely applied for spatial domain identification in spatial transcriptomics (ST), where node representations are learned by aggregating information from ...
IEEE

ACP-MSP: Adaptive Causal Planning with Integrated Multi-Scale Prediction for Autonomous Driving

Abstract: Motion planning for autonomous driving requires consideration of close interactions between the ego-vehicle and surrounding traffic participants, resulting in a synergistic relationship ...
IEEE

On the Problem of Traffic Forecasting Using Graph Neural Networks and Dynamic Adjacency Matrix

Abstract: Accurate short-term traffic flow forecasting is essential for intelligent transportation systems (ITS) and urban operations. However, most graph neural network (GNN) approaches rely on ...
MIT Technology Review

This is the most misunderstood graph in AI

To some, METR’s “time horizon plot” indicates that AI utopia—or apocalypse—is close at hand. The truth is more complicated. MIT Technology Review Explains: Let our writers untangle the complex, messy ...
GitHub

SizheHu/Raster-to-Graph

Raster-to-Graph is a novel automatic recognition framework, which achieves structural and semantic recognition of floorplans, addresses the problem of obtaining high-quality vectorized floorplans from ...