#ecai — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #ecai, aggregated by home.social.
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Advances in temporal graph reasoning to be presented at #ECAI
Researchers from the AI Institute at the University of Stuttgart @Uni_Stuttgart will present a paper tackling key challenges in temporal graph learning. The work, titled “Full-History Graphs with Edge-Type Decoupled Networks for Temporal Reasoning,” will be presented at #ECAI2025, a premier conference in artificial intelligence.
Full-History Graphs with Edge-Type Decoupled Networks for Temporal Reasoning
Temporal graphs are key to understanding dynamic systems—from traffic flow to financial fraud. ETDNet introduces a dual-branch temporal graph neural network that decouples spatial (intra-frame) and temporal (inter-frame) edges.
This design avoids over-smoothing and allows effective long-range reasoning. ETDNet improves driver-intention prediction (75.6% joint accuracy on Waymo) and illicit-transfer detection (88.1% F1 on Elliptic++), while outperforming transformers and memory-bank baselines with fewer parameters and faster training.
O. Mohammed (@osamamohammed), J. Pan, M. Nayyeri, D. Hernández (@daniel), S. Staab. Full-History Graphs with Edge-Type Decoupled Networks for Temporal Reasoning. Proceedings of the 28th European Conference on Artificial Intelligence (ECAI2025). https://arxiv.org/abs/2508.03251
#AI #MachineLearning #TemporalGraphs #TemporalReasoning #ECAI
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Advances in temporal graph reasoning to be presented at #ECAI
Researchers from the AI Institute at the University of Stuttgart @Uni_Stuttgart will present a paper tackling key challenges in temporal graph learning. The work, titled “Full-History Graphs with Edge-Type Decoupled Networks for Temporal Reasoning,” will be presented at #ECAI2025, a premier conference in artificial intelligence.
Full-History Graphs with Edge-Type Decoupled Networks for Temporal Reasoning
Temporal graphs are key to understanding dynamic systems—from traffic flow to financial fraud. ETDNet introduces a dual-branch temporal graph neural network that decouples spatial (intra-frame) and temporal (inter-frame) edges.
This design avoids over-smoothing and allows effective long-range reasoning. ETDNet improves driver-intention prediction (75.6% joint accuracy on Waymo) and illicit-transfer detection (88.1% F1 on Elliptic++), while outperforming transformers and memory-bank baselines with fewer parameters and faster training.
O. Mohammed (@osamamohammed), J. Pan, M. Nayyeri, D. Hernández (@daniel), S. Staab. Full-History Graphs with Edge-Type Decoupled Networks for Temporal Reasoning. Proceedings of the 28th European Conference on Artificial Intelligence (ECAI2025). https://arxiv.org/abs/2508.03251
#AI #MachineLearning #TemporalGraphs #TemporalReasoning #ECAI
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Advances in temporal graph reasoning to be presented at #ECAI
Researchers from the AI Institute at the University of Stuttgart @Uni_Stuttgart will present a paper tackling key challenges in temporal graph learning. The work, titled “Full-History Graphs with Edge-Type Decoupled Networks for Temporal Reasoning,” will be presented at #ECAI2025, a premier conference in artificial intelligence.
Full-History Graphs with Edge-Type Decoupled Networks for Temporal Reasoning
Temporal graphs are key to understanding dynamic systems—from traffic flow to financial fraud. ETDNet introduces a dual-branch temporal graph neural network that decouples spatial (intra-frame) and temporal (inter-frame) edges.
This design avoids over-smoothing and allows effective long-range reasoning. ETDNet improves driver-intention prediction (75.6% joint accuracy on Waymo) and illicit-transfer detection (88.1% F1 on Elliptic++), while outperforming transformers and memory-bank baselines with fewer parameters and faster training.
O. Mohammed (@osamamohammed), J. Pan, M. Nayyeri, D. Hernández (@daniel), S. Staab. Full-History Graphs with Edge-Type Decoupled Networks for Temporal Reasoning. Proceedings of the 28th European Conference on Artificial Intelligence (ECAI2025). https://arxiv.org/abs/2508.03251
#AI #MachineLearning #TemporalGraphs #TemporalReasoning #ECAI
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Advances in temporal graph reasoning to be presented at #ECAI
Researchers from the AI Institute at the University of Stuttgart @Uni_Stuttgart will present a paper tackling key challenges in temporal graph learning. The work, titled “Full-History Graphs with Edge-Type Decoupled Networks for Temporal Reasoning,” will be presented at #ECAI2025, a premier conference in artificial intelligence.
Full-History Graphs with Edge-Type Decoupled Networks for Temporal Reasoning
Temporal graphs are key to understanding dynamic systems—from traffic flow to financial fraud. ETDNet introduces a dual-branch temporal graph neural network that decouples spatial (intra-frame) and temporal (inter-frame) edges.
This design avoids over-smoothing and allows effective long-range reasoning. ETDNet improves driver-intention prediction (75.6% joint accuracy on Waymo) and illicit-transfer detection (88.1% F1 on Elliptic++), while outperforming transformers and memory-bank baselines with fewer parameters and faster training.
O. Mohammed (@osamamohammed), J. Pan, M. Nayyeri, D. Hernández (@daniel), S. Staab. Full-History Graphs with Edge-Type Decoupled Networks for Temporal Reasoning. Proceedings of the 28th European Conference on Artificial Intelligence (ECAI2025). https://arxiv.org/abs/2508.03251
#AI #MachineLearning #TemporalGraphs #TemporalReasoning #ECAI
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Advances in temporal graph reasoning to be presented at #ECAI
Researchers from the AI Institute at the University of Stuttgart @Uni_Stuttgart will present a paper tackling key challenges in temporal graph learning. The work, titled “Full-History Graphs with Edge-Type Decoupled Networks for Temporal Reasoning,” will be presented at #ECAI2025, a premier conference in artificial intelligence.
Full-History Graphs with Edge-Type Decoupled Networks for Temporal Reasoning
Temporal graphs are key to understanding dynamic systems—from traffic flow to financial fraud. ETDNet introduces a dual-branch temporal graph neural network that decouples spatial (intra-frame) and temporal (inter-frame) edges.
This design avoids over-smoothing and allows effective long-range reasoning. ETDNet improves driver-intention prediction (75.6% joint accuracy on Waymo) and illicit-transfer detection (88.1% F1 on Elliptic++), while outperforming transformers and memory-bank baselines with fewer parameters and faster training.
O. Mohammed (@osamamohammed), J. Pan, M. Nayyeri, D. Hernández (@daniel), S. Staab. Full-History Graphs with Edge-Type Decoupled Networks for Temporal Reasoning. Proceedings of the 28th European Conference on Artificial Intelligence (ECAI2025). https://arxiv.org/abs/2508.03251
#AI #MachineLearning #TemporalGraphs #TemporalReasoning #ECAI
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🌱 Making AI Sustainability Visible: Introducing a New DSL for Environmental Impact Documentation
In this work, we aim to leverage the concept of #quality models and service level agreements (#SLAs) common in other IT fields and merge them with existing ML model reporting initiatives and Green/Frugal AI proposals to formalize a Sustainable #QualityModel for AI/ML models.
Work led by Gwendal JOUNEAUX to be presented at the 2nd Workshop on Green-Aware #ArtificialIntelligence (Green-Aware AI) to take place in conjunction with the 28th European Conference on Artificial Intelligence (#ECAI)
🔗 Read the summary: https://modeling-languages.com/sustainability-model-cards-dsl/
📄 Full paper: https://www.arxiv.org/abs/2507.19559
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🌱 Making AI Sustainability Visible: Introducing a New DSL for Environmental Impact Documentation
In this work, we aim to leverage the concept of #quality models and service level agreements (#SLAs) common in other IT fields and merge them with existing ML model reporting initiatives and Green/Frugal AI proposals to formalize a Sustainable #QualityModel for AI/ML models.
Work led by Gwendal JOUNEAUX to be presented at the 2nd Workshop on Green-Aware #ArtificialIntelligence (Green-Aware AI) to take place in conjunction with the 28th European Conference on Artificial Intelligence (#ECAI)
🔗 Read the summary: https://modeling-languages.com/sustainability-model-cards-dsl/
📄 Full paper: https://www.arxiv.org/abs/2507.19559
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🌱 Making AI Sustainability Visible: Introducing a New DSL for Environmental Impact Documentation
In this work, we aim to leverage the concept of #quality models and service level agreements (#SLAs) common in other IT fields and merge them with existing ML model reporting initiatives and Green/Frugal AI proposals to formalize a Sustainable #QualityModel for AI/ML models.
Work led by Gwendal JOUNEAUX to be presented at the 2nd Workshop on Green-Aware #ArtificialIntelligence (Green-Aware AI) to take place in conjunction with the 28th European Conference on Artificial Intelligence (#ECAI)
🔗 Read the summary: https://modeling-languages.com/sustainability-model-cards-dsl/
📄 Full paper: https://www.arxiv.org/abs/2507.19559
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🌱 Making AI Sustainability Visible: Introducing a New DSL for Environmental Impact Documentation
In this work, we aim to leverage the concept of #quality models and service level agreements (#SLAs) common in other IT fields and merge them with existing ML model reporting initiatives and Green/Frugal AI proposals to formalize a Sustainable #QualityModel for AI/ML models.
Work led by Gwendal JOUNEAUX to be presented at the 2nd Workshop on Green-Aware #ArtificialIntelligence (Green-Aware AI) to take place in conjunction with the 28th European Conference on Artificial Intelligence (#ECAI)
🔗 Read the summary: https://modeling-languages.com/sustainability-model-cards-dsl/
📄 Full paper: https://www.arxiv.org/abs/2507.19559
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Are you attending #ECAI 2024? Yunjie He (@royaheeee) will present our paper on generating SROI⁻ ontologies with query embeddings. Query embeddings are used to generalize knowledge graphs and predict answers to queries. We propose a method that, unlike the existing ones for SROI⁻ queries, represents relations with geometric transformations, instead of black box neural operations. In doing so, our method also can generalize the knowledge via ontologies. See you there! #SemanticWeb #KnowledgeGraphs
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Are you attending #ECAI 2024? Yunjie He (@royaheeee) will present our paper on generating SROI⁻ ontologies with query embeddings. Query embeddings are used to generalize knowledge graphs and predict answers to queries. We propose a method that, unlike the existing ones for SROI⁻ queries, represents relations with geometric transformations, instead of black box neural operations. In doing so, our method also can generalize the knowledge via ontologies. See you there! #SemanticWeb #KnowledgeGraphs
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Are you attending #ECAI 2024? Yunjie He (@royaheeee) will present our paper on generating SROI⁻ ontologies with query embeddings. Query embeddings are used to generalize knowledge graphs and predict answers to queries. We propose a method that, unlike the existing ones for SROI⁻ queries, represents relations with geometric transformations, instead of black box neural operations. In doing so, our method also can generalize the knowledge via ontologies. See you there! #SemanticWeb #KnowledgeGraphs
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Are you attending #ECAI 2024? Yunjie He (@royaheeee) will present our paper on generating SROI⁻ ontologies with query embeddings. Query embeddings are used to generalize knowledge graphs and predict answers to queries. We propose a method that, unlike the existing ones for SROI⁻ queries, represents relations with geometric transformations, instead of black box neural operations. In doing so, our method also can generalize the knowledge via ontologies. See you there! #SemanticWeb #KnowledgeGraphs
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Are you attending #ECAI 2024? Yunjie He (@royaheeee) will present our paper on generating SROI⁻ ontologies with query embeddings. Query embeddings are used to generalize knowledge graphs and predict answers to queries. We propose a method that, unlike the existing ones for SROI⁻ queries, represents relations with geometric transformations, instead of black box neural operations. In doing so, our method also can generalize the knowledge via ontologies. See you there! #SemanticWeb #KnowledgeGraphs
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Esta fim-de-semana começa em Santiago de Compostela a 27th European Conference on Artificial Intelligence, na que participam universidades e conferenciantes israelis, ademais de estar patrocinada por empresas e bancos que financiam a guerra e o genocídio em Gaza.
#genocídio #genocide #palestina #palestine #israhell #usc #SantiagoDeCompostela #ecai #ecai2024 #bds
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Esta fim-de-semana começa em Santiago de Compostela a 27th European Conference on Artificial Intelligence, na que participam universidades e conferenciantes israelis, ademais de estar patrocinada por empresas e bancos que financiam a guerra e o genocídio em Gaza.
#genocídio #genocide #palestina #palestine #israhell #usc #SantiagoDeCompostela #ecai #ecai2024 #bds
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Esta fim-de-semana começa em Santiago de Compostela a 27th European Conference on Artificial Intelligence, na que participam universidades e conferenciantes israelis, ademais de estar patrocinada por empresas e bancos que financiam a guerra e o genocídio em Gaza.
#genocídio #genocide #palestina #palestine #israhell #usc #SantiagoDeCompostela #ecai #ecai2024 #bds
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Esta fim-de-semana começa em Santiago de Compostela a 27th European Conference on Artificial Intelligence, na que participam universidades e conferenciantes israelis, ademais de estar patrocinada por empresas e bancos que financiam a guerra e o genocídio em Gaza.
#genocídio #genocide #palestina #palestine #israhell #usc #SantiagoDeCompostela #ecai #ecai2024 #bds
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Esta fim-de-semana começa em Santiago de Compostela a 27th European Conference on Artificial Intelligence, na que participam universidades e conferenciantes israelis, ademais de estar patrocinada por empresas e bancos que financiam a guerra e o genocídio em Gaza.
#genocídio #genocide #palestina #palestine #israhell #usc #SantiagoDeCompostela #ecai #ecai2024 #bds
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Very glad to announce KGPrune, a Web Application to Extract Subgraphs of Interest from Wikidata with Analogical Pruning, accepted as a demo paper at #ECAI 2024! Looking forward to feedback and great use cases from the community!
📺 https://youtu.be/mt5gF4ZmhGY
🌐 https://kgprune.loria.fr/
📎 https://inria.hal.science/hal-04678284v1#knowledgeGraph #artificialIntelligence #semanticWeb #linkedOpenData #knowledgeGraphConstruction #Wikidata
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Very glad to announce KGPrune, a Web Application to Extract Subgraphs of Interest from Wikidata with Analogical Pruning, accepted as a demo paper at #ECAI 2024! Looking forward to feedback and great use cases from the community!
📺 https://youtu.be/mt5gF4ZmhGY
🌐 https://kgprune.loria.fr/
📎 https://inria.hal.science/hal-04678284v1#knowledgeGraph #artificialIntelligence #semanticWeb #linkedOpenData #knowledgeGraphConstruction #Wikidata
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Very glad to announce KGPrune, a Web Application to Extract Subgraphs of Interest from Wikidata with Analogical Pruning, accepted as a demo paper at #ECAI 2024! Looking forward to feedback and great use cases from the community!
📺 https://youtu.be/mt5gF4ZmhGY
🌐 https://kgprune.loria.fr/
📎 https://inria.hal.science/hal-04678284v1#knowledgeGraph #artificialIntelligence #semanticWeb #linkedOpenData #knowledgeGraphConstruction #Wikidata
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Very glad to announce KGPrune, a Web Application to Extract Subgraphs of Interest from Wikidata with Analogical Pruning, accepted as a demo paper at #ECAI 2024! Looking forward to feedback and great use cases from the community!
📺 https://youtu.be/mt5gF4ZmhGY
🌐 https://kgprune.loria.fr/
📎 https://inria.hal.science/hal-04678284v1#knowledgeGraph #artificialIntelligence #semanticWeb #linkedOpenData #knowledgeGraphConstruction #Wikidata
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Very glad to announce KGPrune, a Web Application to Extract Subgraphs of Interest from Wikidata with Analogical Pruning, accepted as a demo paper at #ECAI 2024! Looking forward to feedback and great use cases from the community!
📺 https://youtu.be/mt5gF4ZmhGY
🌐 https://kgprune.loria.fr/
📎 https://inria.hal.science/hal-04678284v1#knowledgeGraph #artificialIntelligence #semanticWeb #linkedOpenData #knowledgeGraphConstruction #Wikidata
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How to choose and deploy industry-specific AI models - DJ Das
Contributor
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How we dodged risks and raised millions for our open-source machine language startup - Jorge Torres
Contributor
Share on Twitter... - http://feedproxy.google.com/~r/Techcrunch/~3/Om-QxOuHKy4/ #artificialintelligence #opensourcesoftware #entrepreneurship #machinelearning #venturecapital #privateequity #startups #eccolumn #echowto #column #ecai #tc