讀書筆記14：Graph networks as learnable physics engines for inference and control（ICML 2018）
原文地址： https://arxiv.org/pdf/1806.01242.pdf 此篇文章有supplementary material，有完整的算法，實現，方法的細節，實驗的視頻 摘要部分：首先還是介紹背景，理解並與日常生活中的各種物理場景進行互動需要豐富的關於世界的結構的知識，這些知識或者是用value or policy function來implicitly的表達，或者是explici
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相關文章
- 1. 讀書筆記13：Neural relational inference for interacting systems（ICML 2018）
- 2. 讀書筆記8：Graph Attention Networks（ICLR 2018)
- 3. 閱讀筆記--CAN GRAPH NEURAL NETWORKS GO 「ONLINE」? AN ANALYSIS OF PRETRAINING AND INFERENCE
- 4. GNN新作《Relational inductive biases，deep learning，and graph networks》讀書筆記
- 5. 《Resolution Adaptive Networks for Effificient Inference》閱讀筆記
- 6. 讀書筆記7：Relational inductive biases, deep learning, and graph networks
- 7. 《Large-Scale Learnable Graph Convolutional Networks》
- 8. Graph Convolutional Networks for Text Classification閱讀筆記
- 9. 讀書筆記----fuzzy control and identification 1
- 10. Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering讀書筆記