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Found 3 papers, 2 papers with code
Disentanglement in Implicit Causal Models via Switch Variable
Learning causal representations from observational and interventional data in the absence of known ground-truth graph structures necessitates implicit latent causal representation learning.
Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting
First, we propose a novel causal model that explains the generative factors in motion forecasting datasets using features that are common across all environments and with features that are specific to each environment.
Deep Representation of Imbalanced Spatio-temporal Traffic Flow Data for Traffic Accident Detection
Automatic detection of traffic accidents has a crucial effect on improving transportation, public safety, and path planning.
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