Search Results for author:
Found 2 papers, 0 papers with code
The Edge of Orthogonality: A Simple View of What Makes BYOL Tick
With simple linear algebra, we show that when using a linear predictor, the optimal predictor is close to an orthogonal projection, and propose a general framework based on orthonormalization that enables to interpret and give intuition on why BYOL works.
Tell me why!—Explanations support learning relational and causal structure
Explanations play a considerable role in human learning, especially in areas that remain major challenges for AI—forming abstractions, and learning about the relational and causal structure of the world.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
