12 citations · 23 across the 4 of their papers we have counts for
4 papers
Causal Deep Learning
M. Alex O. Vasilescu
We derive a set of causal deep neural networks whose architectures are a consequence of tensor (multilinear) factor analysis, a framework that facilitates causal inference. Forward…
Deepfake Representation with Multilinear Regression
Sara Abdali, M. Alex O. Vasilescu, Evangelos E. Papalexakis
Generative neural network architectures such as GANs, may be used to generate synthetic instances to compensate for the lack of real data. However, they may be employed to create m…
CausalX: Causal Explanations and Block Multilinear Factor Analysis
M. Alex O. Vasilescu, Eric Kim, Xiao S. Zeng
By adhering to the dictum, "No causation without manipulation (treatment, intervention)", cause and effect data analysis represents changes in observed data in terms of changes in…
Compositional Hierarchical Tensor Factorization: Representing Hierarchical Intrinsic and Extrinsic Causal Factors
M. Alex O. Vasilescu, Eric Kim
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 ca…