124 citations · 225 across the 18 of their papers we have counts for
16 papers · 1 filter
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…
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…
Position: Curvature Matrices Should Be Democratized via Linear Operators
Felix Dangel, Runa Eschenhagen, Weronika Ormaniec +3
Structured large matrices are prevalent in machine learning. A particularly important class is curvature matrices like the Hessian, which are central to understanding the loss land…
Influence Functions for Scalable Data Attribution in Diffusion Models
Bruno Mlodozeniec, Runa Eschenhagen, Juhan Bae +3
Diffusion models have led to significant advancements in generative modelling. Yet their widespread adoption poses challenges regarding data attribution and interpretability. In th…
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…
Benchmarking Neural Network Training Algorithms
George E. Dahl, Frank Schneider, Zachary Nado +22
Training algorithms, broadly construed, are an essential part of every deep learning pipeline. Training algorithm improvements that speed up training across a wide variety of workl…
Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization
Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen +1
The linearized-Laplace approximation (LLA) has been shown to be effective and efficient in constructing Bayesian neural networks. It is theoretically compelling since it can be see…
Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs
Emilia Magnani, Nicholas Krämer, Runa Eschenhagen +2
Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear solution operator of) partial differential equations (PDEs). The current state of t…
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks
Agustinus Kristiadi, Runa Eschenhagen, Philipp Hennig
Monte Carlo (MC) integration is the de facto method for approximating the predictive distribution of Bayesian neural networks (BNNs). But, even with many MC samples, Gaussian-based…
Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning
Runa Eschenhagen, Erik Daxberger, Philipp Hennig +1
Deep neural networks are prone to overconfident predictions on outliers. Bayesian neural networks and deep ensembles have both been shown to mitigate this problem to some extent. I…
Laplace Redux -- Effortless Bayesian Deep Learning
Erik Daxberger, Agustinus Kristiadi, Alexander Immer +3
Bayesian formulations of deep learning have been shown to have compelling theoretical properties and offer practical functional benefits, such as improved predictive uncertainty qu…