Towards Interpretable Deep Generative Models via Causal Representation Learning

Understand

Recent developments in generative artificial intelligence (AI) rely on machine learning techniques such as deep learning and generative modeling to achieve state-of-the-art performance across wide-ranging domains.

• These methods' surprising performance is due in part to their ability to learn implicit "representations" of complex, multi-modal data.
• Unfortunately, deep neural networks are notoriously black boxes that obscure these representations, making them difficult to interpret or analyze.
• To resolve these difficulties, one approach is to build new interpretable neural network models from the ground up.