2 citations · 2 across the 4 of their papers we have counts for
5 papers
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…
Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective
Wu Lin, Felix Dangel, Runa Eschenhagen +3
Adaptive gradient optimizers like Adam(W) are the default training algorithms for many deep learning architectures, such as transformers. Their diagonal preconditioner is based on…
Structured Inverse-Free Natural Gradient: Memory-Efficient & Numerically-Stable KFAC
Wu Lin, Felix Dangel, Runa Eschenhagen +4
Second-order methods such as KFAC can be useful for neural net training. However, they are often memory-inefficient since their preconditioning Kronecker factors are dense, and num…
Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures
Runa Eschenhagen, Alexander Immer, Richard E. Turner +2
The core components of many modern neural network architectures, such as transformers, convolutional, or graph neural networks, can be expressed as linear layers with $\textit{weig…