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Found 5 papers, 1 papers with code
Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic Models
We empirically show that CausalDiffAE learns a disentangled latent space and is capable of generating high-quality counterfactual images.
From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling
Deep generative models have shown tremendous success in data density estimation and data generation from finite samples.
Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms
We propose ICM-VAE, a framework for learning causally disentangled representations supervised by causally related observed labels.
Neighborhood Random Walk Graph Sampling for Regularized Bayesian Graph Convolutional Neural Networks
The Graph Neural Network (GNN) has proven to be a very useful tool in a variety of graph learning tasks including node classification, link prediction, and edge classification.
A Comparative Study of Transformer-Based Language Models on Extractive Question Answering
Question Answering (QA) is a task in natural language processing that has seen considerable growth after the advent of transformers.
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