Accepted papers:
- Generalizing Orthogonalization for Models with Non-linearities with Chris Kolb and Tobias Weber
- Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI
- Position Paper: Rethinking Empirical Research in Machine Learning: Addressing Epistemic and Methodological Challenges of Experimentation
- Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks? with Emanuel and Lisa
We will upload the paper to Arxiv within the next few days!