56 citations · 140 across the 23 of their papers we have counts for
8 papers · 1 filter
Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks
Alexander Möllers, Alexander Immer, Elvin Isufi +1
Graph contrastive learning has shown great promise when labeled data is scarce, but large unlabeled datasets are available. However, it often does not take uncertainty estimation i…
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…
Learning Layer-wise Equivariances Automatically using Gradients
Tycho F. A. van der Ouderaa, Alexander Immer, Mark van der Wilk
Convolutions encode equivariance symmetries into neural networks leading to better generalisation performance. However, symmetries provide fixed hard constraints on the functions a…
Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion
Alexandru Meterez, Amir Joudaki, Francesco Orabona +3
Normalization layers are one of the key building blocks for deep neural networks. Several theoretical studies have shown that batch normalization improves the signal propagation, b…
Hodge-Aware Contrastive Learning
Alexander Möllers, Alexander Immer, Vincent Fortuin +1
Simplicial complexes prove effective in modeling data with multiway dependencies, such as data defined along the edges of networks or within other higher-order structures. Their sp…
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels
Alexander Immer, Tycho F. A. van der Ouderaa, Mark van der Wilk +2
Selecting hyperparameters in deep learning greatly impacts its effectiveness but requires manual effort and expertise. Recent works show that Bayesian model selection with Laplace…
Improving Neural Additive Models with Bayesian Principles
Kouroche Bouchiat, Alexander Immer, Hugo Yèche +2
Neural additive models (NAMs) enhance the transparency of deep neural networks by handling input features in separate additive sub-networks. However, they lack inherent mechanisms…
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…