This collection is initiated in 2018.
A curated list of awesome deep causal learning methods - when causaliy deep meets deep neural network.
Inspired by awesome-deep-vision, awesome-adversarial-machine-learning, awesome-deep-learning-papers, awesome-architecture-search, awesome-deep-neuroevolution (nice idea for the code index) and awesome-self-supervised-learning.
Learning to inference and disentangle is the next big challenge of Deep Learning.
Welcome to commit and pull request. I will update some guideline on causal software, which could be found out here.
| Title | Authors | Code | Year |
|---|---|---|---|
| A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms | Yoshua Bengio et al. | code | ICLR 2020 |
| Causal Induction from Visual Observations for Goal Directed Tasks | Suraj Nair, et al. | - | arxiv 2019 |
| Granger-causal attentive mixtures of experts: Learning important features with neural networks | Patrick Schwab, et al. | - | AAAI 2019 |
| Causal bandits: Learning good interventions via causal inference | Finnian Lattimore et al. | - | NeurIPS, 2016 |
| Learning granger causality for hawkes processes | Xu ,et al. | - | ICML 2016 |
| Towards a learning theory of cause-effect inference | Lopez Paz, et al. | - | ICML 2015 |
| One-shot learning by inverting a compositional causal process | Brenden M. Lake, et al. | - | NeurIPS 2013 |
| Title | Authors | Code | Year |
|---|---|---|---|
| When Causal Intervention Meets Adversarial Examples and Image Masking for Deep Neural Networks | CHH Yang and YC Liu, et al | code | ICIP 2019 |
| Discovering causal signals in images | Lopez-Paz et al. | code withdrawn from author | CVPR 2017 |
| Causal graph-based video segmentation | Couprie,et al. | - | ICIP 2013 |
C.-H. Huck Yang, Georgia Tech
huckiyang \At \Gatech
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