74 citations · 370 across the 44 of their papers we have counts for
7 papers · 1 filter
Towards Dynamic Feature Acquisition on Medical Time Series by Maximizing Conditional Mutual Information
Fedor Sergeev, Paola Malsot, Gunnar Rätsch +1
Knowing which features of a multivariate time series to measure and when is a key task in medicine, wearables, and robotics. Better acquisition policies can reduce costs while main…
FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning
Tristan Cinquin, Marvin Pförtner, Vincent Fortuin +2
Laplace approximations are popular techniques for endowing deep networks with epistemic uncertainty estimates as they can be applied without altering the predictions of the trained…
How Useful is Intermittent, Asynchronous Expert Feedback for Bayesian Optimization?
Agustinus Kristiadi, Felix Strieth-Kalthoff, Sriram Ganapathi Subramanian +3
Bayesian optimization (BO) is an integral part of automated scientific discovery -- the so-called self-driving lab -- where human inputs are ideally minimal or at least non-blockin…
Gaussian Stochastic Weight Averaging for Bayesian Low-Rank Adaptation of Large Language Models
Emre Onal, Klemens Flöge, Emma Caldwell +2
Fine-tuned Large Language Models (LLMs) often suffer from overconfidence and poor calibration, particularly when fine-tuned on small datasets. To address these challenges, we propo…
On the Challenges and Opportunities in Generative AI
Laura Manduchi, Clara Meister, Kushagra Pandey +23
The field of deep generative modeling has grown rapidly in the last few years. With the availability of massive amounts of training data coupled with advances in scalable unsupervi…
Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks Using the Marginal Likelihood
Rayen Dhahri, Alexander Immer, Betrand Charpentier +2
Neural network sparsification is a promising avenue to save computational time and memory costs, especially in an age where many successful AI models are becoming too large to naïv…