74 citations · 370 across the 44 of their papers we have counts for
20 papers · 1 filter
Data-Driven Discovery of Feature Groups in Clinical Time Series
Fedor Sergeev, Manuel Burger, Polina Leshetkina +3
Clinical time series data are critical for patient monitoring and predictive modeling. These time series are typically multivariate and often comprise hundreds of heterogeneous fea…
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…
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…
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
Theodore Papamarkou, Maria Skoularidou, Konstantina Palla +22
In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language dat…
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…
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…
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…
Incorporating Unlabelled Data into Bayesian Neural Networks
Mrinank Sharma, Tom Rainforth, Yee Whye Teh +1
Conventional Bayesian Neural Networks (BNNs) are unable to leverage unlabelled data to improve their predictions. To overcome this limitation, we introduce Self-Supervised Bayesian…
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…
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.…
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…
On Stein Variational Neural Network Ensembles
Francesco D'Angelo, Vincent Fortuin, Florian Wenzel
Ensembles of deep neural networks have achieved great success recently, but they do not offer a proper Bayesian justification. Moreover, while they allow for averaging of predictio…
Repulsive Deep Ensembles are Bayesian
Francesco D'Angelo, Vincent Fortuin
Deep ensembles have recently gained popularity in the deep learning community for their conceptual simplicity and efficiency. However, maintaining functional diversity between ense…
Annealed Stein Variational Gradient Descent
Francesco D'Angelo, Vincent Fortuin
Particle based optimization algorithms have recently been developed as sampling methods that iteratively update a set of particles to approximate a target distribution. In particul…
DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps
Laura Manduchi, Matthias Hüser, Julia Vogt +2
Generating interpretable visualizations from complex data is a common problem in many applications. Two key ingredients for tackling this issue are clustering and representation le…
MGP-AttTCN: An Interpretable Machine Learning Model for the Prediction of Sepsis
Margherita Rosnati, Vincent Fortuin
With a mortality rate of 5.4 million lives worldwide every year and a healthcare cost of more than 16 billion dollars in the USA alone, sepsis is one of the leading causes of hospi…
SOM-VAE: Interpretable Discrete Representation Learning on Time Series
Vincent Fortuin, Matthias Hüser, Francesco Locatello +2
High-dimensional time series are common in many domains. Since human cognition is not optimized to work well in high-dimensional spaces, these areas could benefit from interpretabl…