1 citations · 3 across the 10 of their papers we have counts for
7 papers · 1 filter
DIPHINE: Diffusion-based -ID Neural Estimator
Simon Pedro Galeano Munoz, Mustapha Bounoua, Giulio Franzese +2
Uncovering the true informational architecture of real-world complex systems requires disentangling how their components uniquely store, redundantly share, and synergistically inte…
TENDE: Transfer Entropy Neural Diffusion Estimation
Simon Pedro Galeano Munoz, Mustapha Bounoua, Giulio Franzese +2
Transfer entropy measures directed information flow in time series, and it has become a fundamental quantity in applications spanning neuroscience, finance, and complex systems ana…
Bridging GANs and Bayesian Neural Networks via Partial Stochasticity
Maurizio Filippone, Marius P. Linhard
Generative Adversarial Networks (GANs) are popular and successful generative models. Despite their success, optimization is notoriously challenging. In this work, we explain the su…
Emergent Granger Causality in Neural Networks: Can Prediction Alone Reveal Structure?
Malik Shahid Sultan, Hernando Ombao, Maurizio Filippone
Granger Causality (GC) offers an elegant statistical framework to study the association between multivariate time series data. Vector autoregressive models (VAR) are simple and eas…
Optimizing Data Augmentation through Bayesian Model Selection
Madi Matymov, Ba-Hien Tran, Michael Kampffmeyer +2
Data Augmentation (DA) has become an essential tool to improve robustness and generalization of modern machine learning. However, when deciding on DA strategies it is critical to c…
Permutation-based Inference for Variational Learning of Directed Acyclic Graphs
Edwin V. Bonilla, Pantelis Elinas, He Zhao +3
Estimating the structure of Bayesian networks as directed acyclic graphs (DAGs) from observational data is a fundamental challenge, particularly in causal discovery. Bayesian appro…
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…