1 citations · 1 across the 4 of their papers we have counts for
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Scalable Mechanistic Neural Networks for Differential Equations and Machine Learning
Jiale Chen, Dingling Yao, Adeel Pervez +2
We propose Scalable Mechanistic Neural Network (S-MNN), an enhanced neural network framework designed for scientific machine learning applications involving long temporal sequences…
Unifying Causal Representation Learning with the Invariance Principle
Dingling Yao, Dario Rancati, Riccardo Cadei +2
Causal representation learning (CRL) aims at recovering latent causal variables from high-dimensional observations to solve causal downstream tasks, such as predicting the effect o…
Marrying Causal Representation Learning with Dynamical Systems for Science
Dingling Yao, Caroline Muller, Francesco Locatello
Causal representation learning promises to extend causal models to hidden causal variables from raw entangled measurements. However, most progress has focused on proving identifiab…
A Sparsity Principle for Partially Observable Causal Representation Learning
Danru Xu, Dingling Yao, Sébastien Lachapelle +4
Causal representation learning aims at identifying high-level causal variables from perceptual data. Most methods assume that all latent causal variables are captured in the high-d…