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Jointly Embedding Multiple Single-Cell Omics Measurements Open
Many single-cell sequencing technologies are now available, but it is still difficult to apply multiple sequencing technologies to the same single cell. In this paper, we propose an unsupervised manifold alignment algorithm, MMD-MA, for in…
Stochastic Doubly Robust Gradient Open
When training a machine learning model with observational data, it is often encountered that some values are systemically missing. Learning from the incomplete data in which the missingness depends on some covariates may lead to biased est…
Auto-Meta: Automated Gradient Based Meta Learner Search Open
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, a…
Unsupervised Visual Attribute Transfer with Reconfigurable Generative Adversarial Networks Open
Learning to transfer visual attributes requires supervision dataset. Corresponding images with varying attribute values with the same identity are required for learning the transfer function. This largely limits their applications, because…
Learning to Discover Cross-Domain Relations with Generative Adversarial Networks Open
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. To…