Unverified50% confidenceFactExact time
Temporal graph neural networks (TGNNs) update node memories from evolving interactions (events) and use these memories for downstream prediction.
1
Sources
50%
Confidence
Long-term
Relevance
10/20/2026
First Seen
Sources
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Related Claims
UnverifiedIn TGNN training, a root event is an event selected as the prediction target, and the model needs to predict the occurrence of that event based on the node memories preceding it; support events are historical events sampled backward to predict the root event, providing context and participating in node memory updates.76% similarUnverifiedLSTM等循环神经网络通过隐状态向量和细胞状态在时间步之间显式传递信息，天然具备序列记忆能力，但无法在序列维度上并行70% similarUnverifiedBuilding a dynamically evolving code knowledge graph requires combining multiple AI technologies such as graph neural networks (GNNs), temporal analysis, and causal inference.69% similarUnverifiedTime per output token (TPOT) measures the speed at which a model generates tokens one by one during the autoregressive decoding phase.69% similarUnverifiedNeural cellular automata replace the update rules of traditional cellular automata with trainable neural networks, enabling the evolution of cell states to be learned from data.68% similar
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