Deriving modular inductive biases from the principle of independent mechanisms
Francesco Locatello
2022 Invited talk
in
Workshop: Dynamic Neural Networks
in
Workshop: Dynamic Neural Networks
Abstract
Causal representation learning tackles the problem of discovering high-level variables from low-level observations. In this talk, I will discuss how modular architectures such as Neural Interpreters and Neural Attentive Circuits implement inductive biases from the causal principle of independent mechanisms. Leveraging dynamic connectivity graphs and conditional computatations, I will showcase their scalability and interesting properties for robust recognition, efficient transfer, and reasoning.
Speaker
Francesco Locatello
Video
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