34 citations · 53 across the 11 of their papers we have counts for
3 papers · 1 filter
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…
Accelerating Non-Conjugate Gaussian Processes By Trading Off Computation For Uncertainty
Lukas Tatzel, Jonathan Wenger, Frank Schneider +1
Non-conjugate Gaussian processes (NCGPs) define a flexible probabilistic framework to model categorical, ordinal and continuous data, and are widely used in practice. However, exac…
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…