·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Vision Hopfield Memory Networks1h◆Stable Deep Reinforcement Learning via Isotropic Gaussian Representations1h◆Minimizing the Hidden Cost of Scales: Graph-Guided Ultra-Low-Bit Quantization for Large Language Models1h◆Insurance of Agentic AI1h◆Geographic Bias and Diversity in AI Evaluation1h◆Do Models Share Safety Representations? Cross-Model Steering for Safe Visual Generation1h◆MLEvolve: A Self-Evolving Framework for Automated Machine Learning Algorithm Discovery1h◆Beyond Rewards in Reinforcement Learning for Cyber Defence1h◆Uncertainty Aware Functional Behavior Prediction and Material Fatigue Assessment for Circular Factory1h◆Residual Modeling for High-Fidelity Learned Compression of Scientific Data1h◆Mutation Without Variation: Convergence Dynamics in LLM-Driven Program Evolution1h◆How Far Did They Go? The Persuasive Tactics of Covert LLM Agents in a Discontinued Field Experiment1h◆What Should Agents Say? Action-state Communication for Efficient Multi-Agent Systems1h◆GITCO: Gated Inference-Time Context Optimization in TSFMs1h◆SentinelBench: A Benchmark for Long-Running Monitoring Agents1h◆An interpretable and trustworthy AI framework for large-scale longitudinal structure-pain association studies using data from the Osteoarthritis Initiative (OAI)1h◆Brick-Composer: Using MLLMs for Assembly with Diverse Bricks1h◆Output Type Before Quality: A Standards-Derived XAI Admissibility Rubric for Autonomous-Driving Safety1h◆PSEBench: A Controllable and Verifiable Benchmark for Evaluating LLMs in Patient Safety Event Triage1h◆Step-by-Step Optimization-like Reasoning in LLMs over Expanding Search Spaces1h◆Vision Hopfield Memory Networks1h◆Stable Deep Reinforcement Learning via Isotropic Gaussian Representations1h◆Minimizing the Hidden Cost of Scales: Graph-Guided Ultra-Low-Bit Quantization for Large Language Models1h◆Insurance of Agentic AI1h◆Geographic Bias and Diversity in AI Evaluation1h◆Do Models Share Safety Representations? Cross-Model Steering for Safe Visual Generation1h◆MLEvolve: A Self-Evolving Framework for Automated Machine Learning Algorithm Discovery1h◆Beyond Rewards in Reinforcement Learning for Cyber Defence1h◆Uncertainty Aware Functional Behavior Prediction and Material Fatigue Assessment for Circular Factory1h◆Residual Modeling for High-Fidelity Learned Compression of Scientific Data1h◆Mutation Without Variation: Convergence Dynamics in LLM-Driven Program Evolution1h◆How Far Did They Go? The Persuasive Tactics of Covert LLM Agents in a Discontinued Field Experiment1h◆What Should Agents Say? Action-state Communication for Efficient Multi-Agent Systems1h◆GITCO: Gated Inference-Time Context Optimization in TSFMs1h◆SentinelBench: A Benchmark for Long-Running Monitoring Agents1h◆An interpretable and trustworthy AI framework for large-scale longitudinal structure-pain association studies using data from the Osteoarthritis Initiative (OAI)1h◆Brick-Composer: Using MLLMs for Assembly with Diverse Bricks1h◆Output Type Before Quality: A Standards-Derived XAI Admissibility Rubric for Autonomous-Driving Safety1h◆PSEBench: A Controllable and Verifiable Benchmark for Evaluating LLMs in Patient Safety Event Triage1h◆Step-by-Step Optimization-like Reasoning in LLMs over Expanding Search Spaces1h◆
News/Efficient Traffic Forecasting on Large-Scale Road Network by Regularized Adaptive Graph Convolution
arxiv
PublishedApril 30, 2026 at 4:00 AM
▲bullish

Efficient Traffic Forecasting on Large-Scale Road Network by Regularized Adaptive Graph Convolution

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2506.07179v2 Announce Type: replace-cross Abstract: Traffic prediction is a critical task in spatial-temporal forecasting with broad applications in travel planning and urban management. To model the complex spatial-temporal dependencies in traffic data, Spatial-Temporal Graph Convolutional Ne

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Mentioned models
02
  • 01
    Spatial-Temporal Graph Convolutional Networks (STGCNs)
  • 02
    Regularized Adaptive Graph Convolution (RAGC)
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#machine-learning#traffic-prediction#graph-convolution#scalability

No replies yet. Be first.

Mentioned models
02
  • 01
    Spatial-Temporal Graph Convolutional Networks (STGCNs)
  • 02
    Regularized Adaptive Graph Convolution (RAGC)
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#machine-learning#traffic-prediction#graph-convolution#scalability

Related coverage

More from ARXIV
arxivVision Hopfield Memory Networks1harxivStable Deep Reinforcement Learning via Isotropic Gaussian Representations1harxivMinimizing the Hidden Cost of Scales: Graph-Guided Ultra-Low-Bit Quantization for Large Language Models1harxivInsurance of Agentic AI1h
The Bubble Brief
WEEKLY

Read machine-learning insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews