24 citations · 168 across the 34 of their papers we have counts for
9 papers · 1 filter
Tactile Image-to-Image Disentanglement of Contact Geometry from Motion-Induced Shear
Anupam K. Gupta, Laurence Aitchison, Nathan F. Lepora
Robotic touch, particularly when using soft optical tactile sensors, suffers from distortion caused by motion-dependent shear. The manner in which the sensor contacts a stimulus is…
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…
BNNpriors: A library for Bayesian neural network inference with different prior distributions
Vincent Fortuin, Adrià Garriga-Alonso, Mark van der Wilk +1
Bayesian neural networks have shown great promise in many applications where calibrated uncertainty estimates are crucial and can often also lead to a higher predictive performance…