The causal visual representation learning is an emerging research topic and has appeared since the 2020s. The related tasks can be roughly categorized into several main aspects: 1) ...

In a paper titled “Towards Causal Representation Learning,” researchers at the Max Planck Institute for Intelligent Systems, the Montreal Institute for Learning Algorithms (Mila), and Google ...

In a paper titled “Towards Causal Representation Learning,” researchers at the Max Planck Institute for Intelligent Systems, the Montreal Institute for Learning Algorithms (Mila), and Google ...

The field of representation learning is concerned with training neural networks to turn such low-level inputs, consisting of millions of individually meaningless variables like pixels, into high ...

Since scientists can't efficiently study how all 20,000 genes interact, they use a technique called causal disentanglement to learn how to combine related groups of genes into a representation ...

An interesting area where AI prediction methods and statistical inference meet is causal machine learning that pays particular attention to inferential quantities. 30-32 Adoption of structural ...

Learning from observational data. ... the researchers conducted simulations to show that the algorithm can efficiently disentangle meaningful causal representations using only observational data. ...