Ev-DTAD improves event-based object detection accuracy and speed by using hierarchical temporal aggregation at the representation level and frequency-aware hypergraph fusion at the model level.
You are allset: A multiset function framework for hypergraph neural networks
2 Pith papers cite this work. Polarity classification is still indexing.
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HyperODE RCA integrates hypergraph learning with latent ODEs and cross-modal attention to improve root cause localization in microservice architectures on the Tianchi AIOps benchmark.
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Rethinking Event-Based Object Dtection through Representation-Level Temporal Aggregation and Model-Level Hypergraph Reasoning
Ev-DTAD improves event-based object detection accuracy and speed by using hierarchical temporal aggregation at the representation level and frequency-aware hypergraph fusion at the model level.
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Hypergraph and Latent ODE Learning for Multimodal Root Cause Localization in Microservices
HyperODE RCA integrates hypergraph learning with latent ODEs and cross-modal attention to improve root cause localization in microservice architectures on the Tianchi AIOps benchmark.