Search Results for author:
Found 4 papers, 0 papers with code
Deep Causal Generative Models with Property Control
This is due to the long-lasting challenge of jointly identifying key latent variables, their causal relations, and their correlation with properties of interest, as well as how to leverage their discoveries toward causally controlled data generation.
Gene-associated Disease Discovery Powered by Large Language Models
The intricate relationship between genetic variation and human diseases has been a focal point of medical research, evidenced by the identification of risk genes regarding specific diseases.
Domain Generalization Deep Graph Transformation
As a result, domain generalization graph transformation that predicts graphs not available in the training data is under-explored, with multiple key challenges to be addressed including (1) the extreme space complexity when training on all input-output mode combinations, (2) difference of graph topologies between the input and the output modes, and (3) how to generalize the model to (unseen) target domains that are not in the training data.
Controllable Data Generation by Deep Learning: A Review
This article is a systematic review that explains this promising research area, commonly known as controllable deep data generation.
