2 citations · 4 across the 4 of their papers we have counts for
11 papers
Clarifying Shampoo: Adapting Spectral Descent to Stochasticity and the Parameter Trajectory
Runa Eschenhagen, Anna Cai, Tsung-Hsien Lee +1
Optimizers leveraging the matrix structure in neural networks, such as Shampoo and Muon, are more data-efficient than element-wise algorithms like Adam and Signum. While in specifi…
Better Hessians Matter: Studying the Impact of Curvature Approximations in Influence Functions
Steve Hong, Runa Eschenhagen, Bruno Mlodozeniec +1
Influence functions offer a principled way to trace model predictions back to training data, but their use in deep learning is hampered by the need to invert a large, ill-condition…
Kronecker-factored Approximate Curvature (KFAC) From Scratch
Felix Dangel, Bálint Mucsányi, Tobias Weber +1
Kronecker-factored approximate curvature (KFAC) is arguably one of the most prominent curvature approximations in deep learning. Its applications range from optimization to Bayesia…
Purifying Shampoo: Investigating Shampoo's Heuristics by Decomposing its Preconditioner
Runa Eschenhagen, Aaron Defazio, Tsung-Hsien Lee +2
The recent success of Shampoo in the AlgoPerf contest has sparked renewed interest in Kronecker-factorization-based optimization algorithms for training neural networks. Despite it…
Spectral-factorized Positive-definite Curvature Learning for NN Training
Wu Lin, Felix Dangel, Runa Eschenhagen +3
Many training methods, such as Adam(W) and Shampoo, learn a positive-definite curvature matrix and apply an inverse root before preconditioning. Recently, non-diagonal training met…
Accelerating Neural Network Training: An Analysis of the AlgoPerf Competition
Priya Kasimbeg, Frank Schneider, Runa Eschenhagen +11
The goal of the AlgoPerf: Training Algorithms competition is to evaluate practical speed-ups in neural network training achieved solely by improving the underlying training algorit…