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Found 6 papers, 2 papers with code
Predicting Cellular Responses with Variational Causal Inference and Refined Relational Information
Predicting the responses of a cell under perturbations may bring important benefits to drug discovery and personalized therapeutics.
Variational Causal Inference
Estimating an individual's potential outcomes under counterfactual treatments is a challenging task for traditional causal inference and supervised learning approaches when the outcome is high-dimensional (e. g. gene expressions, impulse responses, human faces) and covariates are relatively limited.
Graph Convolutional Networks for Multi-modality Medical Imaging: Methods, Architectures, and Clinical Applications
Image-based characterization and disease understanding involve integrative analysis of morphological, spatial, and topological information across biological scales.
Toward heterogeneous information fusion: bipartite graph convolutional networks for in silico drug repurposing
Unlike conventional graph convolution networks always assuming the same node attributes in a global graph, our approach models interdomain information fusion with bipartite graph convolution operation.
Semi-supervised Learning using Adversarial Training with Good and Bad Samples
In this work, we investigate semi-supervised learning (SSL) for image classification using adversarial training.
Semi-supervised learning based on generative adversarial network: a comparison between good GAN and bad GAN approach
Recently, semi-supervised learning methods based on generative adversarial networks (GANs) have received much attention.
