3 papers
stat.ML2025
Position: Solve Layerwise Linear Models First to Understand Neural Dynamical Phenomena (Neural Collapse, Emergence, Lazy/Rich Regime, and Grokking)
Yoonsoo Nam, Seok Hyeong Lee, Clementine C J Domine +5
In physics, complex systems are often simplified into minimal, solvable models that retain only the core principles. In machine learning, layerwise linear models (e.g., linear neur…
stat.ML2024
Decoupling Dynamical Richness from Representation Learning: Towards Practical Measurement
Yoonsoo Nam, Nayara Fonseca, Seok Hyeong Lee +6
Dynamic feature transformation (the rich regime) does not always align with predictive performance (better representation), yet accuracy is often used as a proxy for richness, limi…
cs.LG2024
An exactly solvable model for emergence and scaling laws in the multitask sparse parity problem
Yoonsoo Nam, Nayara Fonseca, Seok Hyeong Lee +2
Deep learning models can exhibit what appears to be a sudden ability to solve a new problem as training time, training data, or model size increases, a phenomenon known as emergenc…