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Found 3 papers, 2 papers with code
Implicit Causal Representation Learning via Switchable Mechanisms
Our approach models the effects of soft interventions by employing a \textit{causal mechanism switch variable} designed to toggle between different causal mechanisms.
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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