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Causal Deep Learning: Causal Capsules and Tensor Transformers
Forward causal questions are addressed with a neural network architecture composed of causal capsules and a tensor transformer.
Neural network architectures for disentangling the multimodal structure of data ensembles
We introduce neural network architectures that model the mechanism that generates data and address the difficult problem of disentangling the multimodal structure of data ensembles.
Deepfake Representation with Multilinear Regression
Generative neural network architectures such as GANs, may be used to generate synthetic instances to compensate for the lack of real data.
CausalX: Causal Explanations and Block Multilinear Factor Analysis
When causal factors are not amenable for active manipulation in the real world due to current technological limitations or ethical considerations, a counterfactual approach performs an intervention on the model of data formation.
Compositional Hierarchical Tensor Factorization: Representing Hierarchical Intrinsic and Extrinsic Causal Factors
Visual objects are composed of a recursive hierarchy of perceptual wholes and parts, whose properties, such as shape, reflectance, and color, constitute a hierarchy of intrinsic causal factors of object appearance.
