·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Theker just raised $85M to build the factory robot that doesn’t specialize in anything56m◆Jeff Bezos’s Prometheus raises $12B to build an ‘artificial general engineer’ for the physical world1h◆SpaceX officially prices shares at $135 in the largest IPO ever6h◆Our new community investments in Virginia support local jobs and expand energy affordability.6h◆SpaceX SPV investors won’t know their true holdings until post-IPO lock-ups lift6h◆Amazon’s data centers used 2.5 billion gallons of water last year9h◆Deezer’s new tool can identify AI music from Spotify, Apple Music, and others10h◆Pool’s new app turns your screenshots into something useful11h◆DoorDash’s new AI chatbot lets you order with prompts and photos12h◆Anthropic apologizes for invisible Claude Fable guardrails15h◆Google DeepMind is worried about what happens when millions of agents start to interact15h◆Deezer launches an AI music detector for other streaming services18h◆Opendoor’s India exit is fueling a bigger conversation about AI and outsourcing22h◆MODF-SIR: A Multi-agent Omni-modal Distilled Framework for Social Intelligence Reasoning22h◆Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!22h◆ARGUS: Stacked Multi-View Identity Mosaic Injection for Subject-Preserving Video Generation22h◆Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions22h◆The Impossibility of Eliciting Latent Knowledge22h◆Mapping Scientific Literature with Large Language Models and Topic Modeling22h◆Grounding Computer Use Agents on Human Demonstrations22h◆Theker just raised $85M to build the factory robot that doesn’t specialize in anything56m◆Jeff Bezos’s Prometheus raises $12B to build an ‘artificial general engineer’ for the physical world1h◆SpaceX officially prices shares at $135 in the largest IPO ever6h◆Our new community investments in Virginia support local jobs and expand energy affordability.6h◆SpaceX SPV investors won’t know their true holdings until post-IPO lock-ups lift6h◆Amazon’s data centers used 2.5 billion gallons of water last year9h◆Deezer’s new tool can identify AI music from Spotify, Apple Music, and others10h◆Pool’s new app turns your screenshots into something useful11h◆DoorDash’s new AI chatbot lets you order with prompts and photos12h◆Anthropic apologizes for invisible Claude Fable guardrails15h◆Google DeepMind is worried about what happens when millions of agents start to interact15h◆Deezer launches an AI music detector for other streaming services18h◆Opendoor’s India exit is fueling a bigger conversation about AI and outsourcing22h◆MODF-SIR: A Multi-agent Omni-modal Distilled Framework for Social Intelligence Reasoning22h◆Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!22h◆ARGUS: Stacked Multi-View Identity Mosaic Injection for Subject-Preserving Video Generation22h◆Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions22h◆The Impossibility of Eliciting Latent Knowledge22h◆Mapping Scientific Literature with Large Language Models and Topic Modeling22h◆Grounding Computer Use Agents on Human Demonstrations22h◆
Tag

#graph-learning

3 articles tagged #graph-learning

arxivMay 22bullish

Billion-Scale Graph Foundation Models

arXiv:2602.04768v2 Announce Type: replace Abstract: Graph-structured data underpins many critical applications. While foundation models have transformed language and vision via large-scale pretraining and lightweight adaptation, extending this paradigm to general, real-world graphs is challenging. I

GR1 model#graph-learning#foundation-models#pretrainingRead on arxiv →
arxivMay 21bullish

Fast and Featureless Node Representation Learning with Partial Pairwise Supervision

arXiv:2605.19916v1 Announce Type: cross Abstract: We introduce Contrastive FUSE, a fast and unified framework for scalable node representation learning in graphs with partially available pairwise node labels and no available node features. Unlike existing methods, we directly optimize a spectral con

CO1 model#machine-learning#graph-learning#optimizationRead on arxiv →
arxivApr 29

Latent-Hysteresis Graph ODEs: Modeling Coupled Topology-Feature Evolution via Continuous Phase Transitions

arXiv:2604.24293v1 Announce Type: cross Abstract: Graph neural ordinary differential equations (Graph ODEs) extend graph learning from discrete message-passing layers to continuous-time representation flows. While it supports adaptive long-range propagation, we show that Graph ODEs with strictly pos

GRHY2 models#graph-learning#machine-learning#artificial-intelligenceRead on arxiv →
HomeModelsNews