1.3 Knowledge Transfer with Generated Fake Graphs 首先更新特征参数 H 随机结构参数 \Theta 。然后使用 H 作为节点特征,从 P_\Theta(A) 中采样生成拓扑结构,从而获得 fake graph data。Teacher GNN Model 在这些图上输出高置信度类概率,因此知识更有可能集中在这些图上。然后使用 KL-divergence 将知识从 Teacher...
因此提出一种通过分层强化学习的自由方向知识蒸馏框架FreeKD,如下图; FreeKD框架 上述框架中,分层强化学习可以被视为一个由两个级别动作组成的强化知识判断:(1)node-level action(节点级别动作),决定用于传播软标签的每个节点的蒸馏方向;(2)structural-level action(结构级别动作),确定要传播通过节点即动作生成的局部...
Graph-free knowledge distillation for graph neural networks. arXiv 2021, arXiv:2105.07519. [Google Scholar] Yang, Y.; Qiu, J.; Song, M.; Tao, D.; Wang, X. Distilling knowledge from graph convolutional networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern ...
Accurate Prediction of Free Solvation Energy of Organic Molecules via Graph Attention Network and Message Passing Neural Network from Pairwise Atomistic Interactions,Ramin Ansari, Amirata Ghorbani DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction,Alex Morehead, Chen...
GFKDGraph-Free Knowledge Distillation for Graph Neural Networks∗Paper2021 IJCAI LWC-KDGraph Structure Aware Contrastive Knowledge Distillation for Incremental Learning in Recommender SystemsPaper2021 CIKM EGADEGAD: Evolving Graph Representation Learning with Self-Attention and Knowledge Distillation for Live ...
To overcome these limitations, we propose SynthKG, a multi-step, document-level ontology-free KG synthesis workflow based on LLMs. By fine-tuning a smaller LLM on the synthesized document-KG pairs, we streamline the multi-step process into a single-step KG generation approach called Distill-...
《Online Knowledge Distillation with Diverse Peers》(AAAI 2020) GitHub:O网页链接《Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs》(TACL 2020) GitHub:O网页链接《ZeroCostDL4Mic: exploiting Google Colab to develop a free and open-source toolbox for Deep-Learning in...
Knowledge distillation is a type of regularization acting on the loss function: Sheet-metalNet uses the following multi-class cross-entropy loss function to encourage the output vector \(\hat{y}\) to be consistent with the ground truth y: $$\begin{aligned} L_{hard}\left( y,\hat{y}\rig...
Li, C., et al.: Knowledge condensation distillation. In: European Conference on Computer Vision, pp. 19–35. Springer, Cham (2022) Google Scholar Li, C., et al.: Domain generalization on medical imaging classification using episodic training with task augmentation. Comput. Biol. Med. 141,...
Tinygnn: Learning efficient graph neural networks[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2020: 1848-1856. [12] Deng X, Zhang Z. Graph-free knowledge distillation for graph neural networks[J]. arXiv preprint arXiv:2105.07519, 2021....