Showing stat.MLShow all
2 papers · 1 filter
stat.ML2026
On the Loss Landscape Geometry of Regularized Deep Matrix Factorization: Uniqueness and Sharpness
Anil Kamber, Rahul Parhi
Weight decay is ubiquitous in training deep neural network architectures. Its empirical success is often attributed to capacity control; nonetheless, our theoretical understanding…
stat.ML2026
Sharpness of Minima in Deep Matrix Factorization
Anil Kamber, Rahul Parhi
Understanding the geometry of the loss landscape near a minimum is key to explaining the implicit bias of gradient-based methods in non-convex optimization problems such as deep ne…