Visual representation learning is ubiquitous in various real-world applications, including
visual comprehension, video understanding, multi-modal analysis, human-computer …

Learning disentanglement aims at finding a low dimensional representation which consists
of multiple explanatory and generative factors of the observational data. The framework of …

Causal induction, ie, identifying unobservable mechanisms that lead to the observable
relations among variables, has played a pivotal role in modern scientific discovery …

Causal Machine Learning (CausalML) is an umbrella term for machine learning methods
that formalize the data-generation process as a structural causal model (SCM). This …

We present a novel unsupervised feature representation learning method, Visual
Commonsense Region-based Con-volutional Neural Network (VC R-CNN), to serve as an …

Inducing causal relationships from observations is a classic problem in machine learning.
Most work in causality starts from the premise that the causal variables themselves are …

The two fields of machine learning and graphical causality arose and are developed
separately. However, there is, now, cross-pollination and increasing interest in both fields to …

Model explanations based on pure observational data cannot compute the effects of
features reliably, due to their inability to estimate how each factor alteration could affect the …

We introduce a framework for learning robust visual representations that generalize to new
viewpoints, backgrounds, and scene contexts. Discriminative models often learn naturally …

Abstract We introduce Iterated Integrated Attributions (IIA)-a generic method for explaining
the predictions of vision models. IIA employs iterative integration across the input image, the …