Unimodal model representations converge to a relational structure captured by the Indra representation via V-enriched Yoneda embedding, which is unique and structure-preserving and improves cross-model and cross-modal robustness when instantiated with angular distance.
Graph attention networks.stat, 1050(20):10–48550
3 Pith papers cite this work. Polarity classification is still indexing.
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Time-expanded interaction graphs with graph neural networks enable real-time prediction of surgical procedure duration deviations from team communication patterns and support counterfactual identification of beneficial changes.
GAMMA-Net combines Graph Attention Networks and multi-axis Mamba to outperform prior models in long-horizon traffic forecasting, with up to 16.25% lower MAE on benchmarks like METR-LA and PEMS datasets.
citing papers explorer
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The Indra Representation Hypothesis for Multimodal Alignment
Unimodal model representations converge to a relational structure captured by the Indra representation via V-enriched Yoneda embedding, which is unique and structure-preserving and improves cross-model and cross-modal robustness when instantiated with angular distance.
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Actionable Real-Time Modeling of Surgical Team Dynamics via Time-Expanded Interaction Graphs
Time-expanded interaction graphs with graph neural networks enable real-time prediction of surgical procedure duration deviations from team communication patterns and support counterfactual identification of beneficial changes.
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GAMMA-Net: Adaptive Long-Horizon Traffic Spatio-Temporal Forecasting Model based on Interleaved Graph Attention and Multi-Axis Mamba
GAMMA-Net combines Graph Attention Networks and multi-axis Mamba to outperform prior models in long-horizon traffic forecasting, with up to 16.25% lower MAE on benchmarks like METR-LA and PEMS datasets.