This paper introduces several advancements for continuous and distributed object-centric representations, scaling them from simple toy to real-world data.
We present iCITRIS, a causal representation learning method that can identify causal variables with instantaneous effects and their graph from temporal sequences. Published at ICLR 2023.
We present CITRIS, a causal representation learning algorithm for multidimensional causal factors identified from videos with interventions. Published at ICML 2022 (**Spotlight**).
We perform unsupervised object-centric representation learning by exploiting complex-valued activations in an unconstrained autoencoder architecture.