24 citations · 168 across the 34 of their papers we have counts for
19 papers · 1 filter
An Improved Variational Approximate Posterior for the Deep Wishart Process
Sebastian Ober, Ben Anson, Edward Milsom +1
Deep kernel processes are a recently introduced class of deep Bayesian models that have the flexibility of neural networks, but work entirely with Gram matrices. They operate by al…
Decision trees compensate for model misspecification
Hugh Panton, Gavin Leech, Laurence Aitchison
The best-performing models in ML are not interpretable. If we can explain why they outperform, we may be able to replicate these mechanisms and obtain both interpretability and per…
A theory of representation learning gives a deep generalisation of kernel methods
Adam X. Yang, Maxime Robeyns, Edward Milsom +3
The successes of modern deep machine learning methods are founded on their ability to transform inputs across multiple layers to build good high-level representations. It is theref…
A variational approximate posterior for the deep Wishart process
Sebastian W. Ober, Laurence Aitchison
Recent work introduced deep kernel processes as an entirely kernel-based alternative to NNs (Aitchison et al. 2020). Deep kernel processes flexibly learn good top-layer representat…
InfoNCE is variational inference in a recognition parameterised model
Laurence Aitchison, Stoil Ganev
Here, we show that the InfoNCE objective is equivalent to the ELBO in a new class of probabilistic generative model, the recognition parameterised model (RPM). When we learn the op…
Data augmentation in Bayesian neural networks and the cold posterior effect
Seth Nabarro, Stoil Ganev, Adrià Garriga-Alonso +3
Bayesian neural networks that incorporate data augmentation implicitly use a ``randomly perturbed log-likelihood [which] does not have a clean interpretation as a valid likelihood…