Causal Representation, Now Getting Its Due Importance in Machine Learning

Researchers at work find a way to enhance AI systems working through causal learning to overcome challenges.

Bernhard Scholkopf and Stefan Bauer from Max Planck Institute for Intelligent Systems; Francesco Locatello and Nal Kalchbrenner as Google researchers; Yoshua Bengio, Nan Rosemary Ke, and Anirudh Goyal from Montreal Institute for Learning Algorithms (Mila) came together for research.

The research paper titled “Towards Causal Representation Learning” provides the way through which the artificial intelligent systems can learn causal representations and how the absence of the same in machine learning algorithms and models is giving rise to challenges in front of us.

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