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Machine Learning for Physics and the Physics of Learning 2019 Workshop II: In this episode of Innovation Insights, Emerald's Sector Specialist Graham Carey discusses 'Materials informatics' with SymbolicRegression.jl is a state-of-the-art symbolic regression library written from scratch in How can we explain how deep neural networks arrive at decisions? Feature representation is complex and to the human eye ... This talk was part of SciMLCon 2022! For more information, For more information on the ...
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Julia Ling: "Machine Learning for Materials Discovery" | IACS Seminar
Interpretability: Understanding how AI models think
Interpretable Machine Learning with SymbolicRegression.jl | Miles Cranmer | JuliaCon 2023
Model Interpretability in Machine Learning [Google ML Summit]
Julia Ling interviewed by Brian Whitman at Teen Choice Awards Gifting Suite
ExplainableAI.jl: Interpreting Neural Networks in Julia | Adrian Hill | JuliaCon 2022
JULIA LING ("Chuck" "ER") attends CAPE's HOLIDAY SOIREE IN HOLLYWOOD
Internals of ModelingToolkit | Yingbo Ma | SciMLCon 2022
Anna Wu's Return to the Buy More
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Last Updated: June 12, 2026
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