【論文筆記】2017-NIPS-Causal Effect Inference with Deep Latent-Variable Models
Causal Effect Inference with Deep Latent-Variable Models 筆者最近在做causal inference這個方向，因此會把日常讀到的還(neng)不(kan)錯(dong)的paper簡單整理一下做個筆記，歡迎感興趣的童鞋交流討論~ 背景 Causal inference涉及到的數據集通常由三個變量組成 { X , T , Y } \left\
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相關文章
- 1. 【論文筆記】2019-WWW-Multiple Treatment Effect Estimation using Deep Generative Model with Task Embedding
- 2. 論文筆記-An Analysis of Deep Neural Network Models for Practical Applications
- 3. 【論文筆記】Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
- 4. 論文筆記：Membership Inference Attacks Against Machine Learning Models
- 5. 論文Autoencoding variational inference for topic models閱讀筆記
- 6. Deep Anomaly Detection with Deviation Networks 論文筆記
- 7. 論文筆記：《Playing Atari with Deep Reinforcement Learning》
- 8. Deep Learning Models on Kubernetes with GPUs
- 9. 論文閱讀Recursive Deep Models
- 10. 論文筆記：Quantizing deep convolutional networks for efficient inference: A whitepaper