Interaction Asymmetry: A General Principle for Learning Composable Abstractions | Robust Machine Learning – Max Planck Institute for Intelligent Systems

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Comparative Vision Science

Does CLIP's Generalization Performance Mainly Stem from High Train-Test Similarity?

In Search of Forgotten Domain Generalization

Partial Success in Closing the Gap between Human and Machine Vision

Foundations of Machine Learning

Cross-Entropy Is All You Need To Invert the Data Generating Process

Contrastive Learning Inverts the Data Generating Process

Interaction Asymmetry: A General Principle for Learning Composable Abstractions

Mechanistic Interpretability

Measuring Per-Unit Interpretability at Scale Without Humans

Exemplary Natural Images explain CNN Activations better than State-Of-The-Art Feature Visualizations

How Well do Feature Visualizations Support Causal Understanding of CNN Activations?

Robust Machine Learning

Interaction Asymmetry: A General Principle for Learning Composable Abstractions

Interaction asymm

We propose interaction asymmetry---parts of the same concept have more complex interactions than parts of different concepts---as a general principle for provable disentanglement and compositional generalization of concepts without supervision~[

].