74 citations · 370 across the 44 of their papers we have counts for
17 papers · 1 filter
Pathologies in priors and inference for Bayesian transformers
Tristan Cinquin, Alexander Immer, Max Horn +1
In recent years, the transformer has established itself as a workhorse in many applications ranging from natural language processing to reinforcement learning. Similarly, Bayesian…
Probing as Quantifying Inductive Bias
Alexander Immer, Lucas Torroba Hennigen, Vincent Fortuin +1
Pre-trained contextual representations have led to dramatic performance improvements on a range of downstream tasks. Such performance improvements have motivated researchers to qua…
Sparse MoEs meet Efficient Ensembles
James Urquhart Allingham, Florian Wenzel, Zelda E Mariet +10
Machine learning models based on the aggregated outputs of submodels, either at the activation or prediction levels, often exhibit strong performance compared to individual models.…
Deep Classifiers with Label Noise Modeling and Distance Awareness
Vincent Fortuin, Mark Collier, Florian Wenzel +7
Uncertainty estimation in deep learning has recently emerged as a crucial area of interest to advance reliability and robustness in safety-critical applications. While there have b…
Neural Variational Gradient Descent
Lauro Langosco di Langosco, Vincent Fortuin, Heiko Strathmann
Particle-based approximate Bayesian inference approaches such as Stein Variational Gradient Descent (SVGD) combine the flexibility and convergence guarantees of sampling methods wi…
Quantum Bayesian Neural Networks
Noah Berner, Vincent Fortuin, Jonas Landman
Quantum machine learning promises great speedups over classical algorithms, but it often requires repeated computations to achieve a desired level of accuracy for its point estimat…