5 citations · 6 across the 20 of their papers we have counts for
18 papers · 1 filter
Guiding Posterior Exploration with Optimizer-Derived Geometry
Moritz Schlager, Emanuel Sommer, Thomas Möllenhoff +1
Sampling-based methods offer a principled approach to uncertainty quantification in Bayesian neural networks. Their practical use, however, is often challenged by the computational…
SOAP-Bubbles: Structured Weight Uncertainty for Neural Networks
Adrian Robert Minut, Nico Daheim, Marco Miani +3
Structured weight-uncertainty can improve many aspects of deep learning, but it remains costly to estimate and difficult to implement. Here, we show that these issues can be addres…
Calibrated Sampling-Free Uncertainty Estimation in Bayesian Deep Learning
Tobias Jan Wieczorek, Leon de Andrade, Thomas Möllenhoff +1
Modern deep learning models remain notoriously prone to overconfidence, limiting their reliability in high-stakes applications. Bayesian methods aim to counter this by learning a d…
A Stein Identity for q-Gaussians with Bounded Support
Sophia Sklaviadis, Thomas Moellenhoff, Andre F. T. Martins +2
Stein's identity is a fundamental tool in machine learning with applications in generative models, stochastic optimization, and other problems involving gradients of expectations u…
Optimization Guarantees for Square-Root Natural-Gradient Variational Inference
Navish Kumar, Thomas Möllenhoff, Mohammad Emtiyaz Khan +1
Variational inference with natural-gradient descent often shows fast convergence in practice, but its theoretical convergence guarantees have been challenging to establish. This is…
Log-Normal Multiplicative Dynamics for Stable Low-Precision Training of Large Networks
Keigo Nishida, Eren Mehmet Kıral, Kenichi Bannai +2
Studies in neuroscience have shown that biological synapses follow a log-normal distribution whose transitioning can be explained by noisy multiplicative dynamics. Biological netwo…