Yoon Lab @ Hanyang

Yoon Lab @ Hanyang

Neuro-inspired AI group

Our group is interested in understanding principles that underlie learning and inference in the brain and machines, mainly by using tools from machine learning and statistics.

1. Probabilistic Graphical Models

Probabilistic graphical models (PGMs) can efficiently represent the structure of many complex data and processes by making explicit conditional independences among random variables. We use PGMs as an adequate generative structure (inductive biases), allowing for rich integration into more complex systems.

2. Deep Learning

Deep learning is a representation-learning method with special emphasis on compositionality. We use its capability of learning complex functions with end-to-end design philosophy to solve complex structured problems.

3. Computational Neuroscience

What kinds of network connectivity or organization support integration, memory, and gating in the brain? We study these questions through the aforementioned tools.

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