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Found 4 papers, 1 papers with code
Stochastic Doubly Robust Gradient
When training a machine learning model with observational data, it is often encountered that some values are systemically missing.
Auto-Meta: Automated Gradient Based Meta Learner Search
Fully automating machine learning pipelines is one of the key challenges of current artificial intelligence research, since practical machine learning often requires costly and time-consuming human-powered processes such as model design, algorithm development, and hyperparameter tuning.
Unsupervised Visual Attribute Transfer with Reconfigurable Generative Adversarial Networks
To address the issue, we propose an unsupervised method to learn to transfer visual attribute.
Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
While humans easily recognize relations between data from different domains without any supervision, learning to automatically discover them is in general very challenging and needs many ground-truth pairs that illustrate the relations.
Ranked #10 on
Facial Expression Translation
on CelebA
