#graphneuralnetworks wyniki wyszukiwania
"Non-convolutional Graph Neural Networks" by @YuanqingWang , and @kchonyc Paper: arxiv.org/abs/2408.00165 #graphneuralnetworks
Following the "Tutorial on #UserProfiling with #GraphNeuralNetworks and Related Beyond-Accuracy Perspectives" at #UMAP2023 by @erasmopurif11, @ludovicoboratto and @ernestowdeluca 🚀
"Homomorphism Counts as Structural Encodings for Graph Learning" by @mmbronstein, @ismaililkanc, @mat_lanzinger et al. #MachineLearning #graphneuralnetworks
"Scalable Message Passing Neural Networks: No Need for Attention in Large Graph Representation Learning" by @ocariz__ ,@ottogin1 , Anastasis Kratsios, @mmbronstein,@epomqo Paper: arxiv.org/abs/2411.00835 #graphneuralnetworks
"GraphAny: A Foundation Model for Node Classification on Any Graph" by @AndyJiananZhao, @michael_galkin, @mmbronstein, @zhu_zhaocheng, @tangjianpku et al. Paper: arxiv.org/abs/2405.20445 #graphneuralnetworks #foundationmodels
It has been a long time since I did an online course 👨🎓 . I was focused on my work this year with #GraphNeuralNetworks as my niche and never got the time or the mental fortitude to catch up to the intimidating and overwhelming amount of progress being made in the #LLM and #genai…
"Future Directions in Foundations of Graph Machine Learning" by @HaggaiMaron ,@ismaililkanc, @ffabffrasca, @dereklim_lzh, @mmbronstein et al. Paper: arxiv.org/abs/2402.02287 #graphneuralnetworks
"Link Prediction with Relational Hypergraphs" by @hxyscott, @mmbronstein , @ismaililkanc et al. Paper: arxiv.org/abs/2402.04062 #graphneuralnetworks
"Graph Low-Rank Adapters of High Regularity for Graph Neural Networks and Graph Transformers" by Pantelis Papageorgiou, @ocariz__, Anastasis Kratsios, @mmbronstein Paper: openreview.net/forum?id=gxhZj… Code: github.com/PanPapag/GConv… #graphneuralnetworks
"Enhancing the Expressivity of Temporal Graph Networks through Source-Target Identification" by @AaronTjandra, @fedzbar and @mmbronstein Paper: arxiv.org/abs/2411.03596 #graphneuralnetworks
Using an approach that resembles dynamical mean-field theory, researchers detail how the architecture of a graph convolutional network must scale with depth to avoid oversmoothing and demonstrate how to deal with continuous #GraphNeuralNetworks. 🔗 go.aps.org/3XlaX2d
"Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality" by @JoshSouthern13, @ytn_ym, Guy Bar-Shalom, @mmbronstein, @HaggaiMaron, @ffabffrasca Paper: arxiv.org/abs/2501.03113 #graphneuralnetworks
"Towards Quantifying Long-Range Interactions in Graph Maine Learning: a Large Graph Dataset and a Measurement" by Huidong Liang, @ocariz__, Baskaran Sripathmanathan, @mmbronstein, @epomqo Paper: arxiv.org/abs/2503.09008 #graphneuralnetworks #machinelearning
There's a lot of goodness in #GraphNeuralNetworks including better model quality over traditional #MachineLearning, you can learn more here: hubs.ly/Q025lsJg0
"Learning Latent Graph Structures and their Uncertainty" by @alle_manenti, @dan_zambon, and Cesare Alippi Paper: arxiv.org/abs/2405.19933 #graphneuralnetworks
#Today’s lecture that introduces the key concepts of the graph theory and demonstrates the inherent expressive power of graphs! “#GraphNeuralNetworks: From Fundamentals to Physics application” by Ilias Tsaklidis at 13:15 CEST Tune in from here: indico.cern.ch/event/1293861/
Want to try #GraphNeuralNetworks? Or expand your expertise? Join our #GNNs Masterclass to kick off 2023 with crystal-clear vision on how you can succeed with GNNs. Featuring: ✔️ GNN performance tips ✔️ OGB-winning models ✔️ Free GNN access in IPU cloud hubs.la/Q01wRftb0
Exploring the Future of Graph Neural Networks: Trends, Trade-offs, and Industry Use Cases #GraphNeuralNetworks #GNNs #DataAnalysis #IndustryUseCases #GraphTheory #EmergingTechnologies #DataScience #ArtificialIntelligence #TrendsInTechnology #Futurism
"Message-Passing Monte Carlo: Generating low-discrepancy point sets via Graph Neural Networks" by @tk_rusch, Nathan Kirk PhD, @mmbronstein, Christiane Lemieux, and Daniela Rus Paper: arxiv.org/abs/2405.15059 #graphneuralnetworks #machinelearning #numericalanalysis
Direction Improves Graph Learning #geometricdeeplearning #editorspick #graphneuralnetworks dlvr.it/SqMfc9
Forget just processing data points! The latest Neural Networks, called GNNs, now understand complex *relationships* between data, like how your brain maps social connections. This unlocks wild new insights! 🤯 #GraphNeuralNetworks #AITrends
Enhanced #Encrypted Traffic Analysis Leveraging #GraphNeuralNetworks and Optimized Feature #DimensionalityReduction ✏️ In-Su Jung et al. 🔗 brnw.ch/21wXRBG Viewed: 3357; Cited: 5 #mdpisymmetry #GraphSAGE #metadata @ComSciMath_Mdpi
“Graph Neural Networks in Action.” 📖 Available now — read it online on NJD Web Download! #GraphNeuralNetworks #MachineLearning #ArtificialIntelligence #ScienceBook #DeepLearning #NJDWebDownload #ReadOnline #TechInnovation #AIResearch #NeuralNetworks #DataScience #GraphAI
Following the "Tutorial on #UserProfiling with #GraphNeuralNetworks and Related Beyond-Accuracy Perspectives" at #UMAP2023 by @erasmopurif11, @ludovicoboratto and @ernestowdeluca 🚀
"Non-convolutional Graph Neural Networks" by @YuanqingWang , and @kchonyc Paper: arxiv.org/abs/2408.00165 #graphneuralnetworks
Everything is Connected: Graph Neural Networks Petar Veličković : arxiv.org/abs/2301.08210 #ArtificialIntelligence #GNN #GraphNeuralNetworks
Scaling #GraphNeuralNetworks to massive datasets takes more than compute power; it takes coordination. This @ProceedingsIEEE #PIEEEKnowledgeInFocus article spotlight explores how distributed training is redefining what #GNNs can achieve at scale: bit.ly/ProceedingsIEE…
Everything is Connected: Graph Neural Networks Petar Veličković : arxiv.org/abs/2301.08210 #ArtificialIntelligence #GNN #GraphNeuralNetworks
Everything is Connected: Graph Neural Networks Petar Veličković : arxiv.org/abs/2301.08210 #ArtificialIntelligence #GNN #GraphNeuralNetworks
How does over-squashing affect the power of GNNs? Di Giovanni et al.: arxiv.org/abs/2306.03589 #GraphNeuralNetworks #GNN #DeepLearning
Everything is Connected: Graph Neural Networks Petar Veličković : arxiv.org/abs/2301.08210 #ArtificialIntelligence #GNN #GraphNeuralNetworks
Benchmarking Graph Neural Networks Dwivedi et al.: arxiv.org/abs/2003.00982 #ArtificialIntelligence #DeepLearning #GraphNeuralNetworks
How does over-squashing affect the power of GNNs? Di Giovanni et al.: arxiv.org/abs/2306.03589 #GraphNeuralNetworks #GNN #DeepLearning
How does over-squashing affect the power of GNNs? Di Giovanni et al.: arxiv.org/abs/2306.03589 #GraphNeuralNetworks #GNN #DeepLearning
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection Jin et al.: arxiv.org/abs/2307.03759 #DeepLearning #GraphNeuralNetworks #TimeSeries
"Homomorphism Counts as Structural Encodings for Graph Learning" by @mmbronstein, @ismaililkanc, @mat_lanzinger et al. #MachineLearning #graphneuralnetworks
"Future Directions in Foundations of Graph Machine Learning" by @HaggaiMaron ,@ismaililkanc, @ffabffrasca, @dereklim_lzh, @mmbronstein et al. Paper: arxiv.org/abs/2402.02287 #graphneuralnetworks
"GraphAny: A Foundation Model for Node Classification on Any Graph" by @AndyJiananZhao, @michael_galkin, @mmbronstein, @zhu_zhaocheng, @tangjianpku et al. Paper: arxiv.org/abs/2405.20445 #graphneuralnetworks #foundationmodels
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection Jin et al.: arxiv.org/abs/2307.03759 #DeepLearning #GraphNeuralNetworks #TimeSeries
🚀 Call for Papers | Special Issue in Electronics Advances in Graph Neural #Networks for #Spatiotemporal Forecasting Submission Deadline: 15 April 2026 👥 Guest Editors: Zhou Zhou, Zhiwen Shao mdpi.com/journal/electr… #GraphNeuralNetworks #GNN #SpatiotemporalForecasting
Are you looking to harness #AI breakthroughs, such as #LanguageModels, #GraphNeuralNetworks, to enhance your materials development processes? Join us for our upcoming live virtual course, Machine Learning for Materials Informatics. Learn more:bit.ly/3zjTHit.
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