Dynamic Explainability through Dynamic Causal Modeling
- Track: AI and Machine Learning devroom
- Room: UB2.252A (Lameere)
- Day: Sunday
- Start: 12:30
- End: 12:45
- Video only: ub2252a
- Chat: Join the conversation!
Dynamic Causal Modeling is a uncertainty aware, explainable AI technique that uses physics inspired "dynamical systems" to explore time series data.
In this talk we discuss the theory and practice of Dynamic Causal Modeling, and the work we've done to take this code outside of it's research rooted MATLAB implementation into a robust, general piece of software under a FOSS license.
Speakers
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William Jones |