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stat.ML2026
Amortising Inference and Meta-Learning Priors in Neural Networks
Tommy Rochussen, Vincent Fortuin
One of the core facets of Bayesianism is in the updating of prior beliefs in light of new evidenceso how can we maintain a Bayesian approach if we have no prior belief…
stat.ML2025
Sparse Gaussian Neural Processes
Tommy Rochussen, Vincent Fortuin
Despite significant recent advances in probabilistic meta-learning, it is common for practitioners to avoid using deep learning models due to a comparative lack of interpretability…
stat.ML2024
Structured Partial Stochasticity in Bayesian Neural Networks
Tommy Rochussen
Bayesian neural network posterior distributions have a great number of modes that correspond to the same network function. The abundance of such modes can make it difficult for app…