24 citations · 168 across the 34 of their papers we have counts for
4 papers · 1 filter
Machine learning emulation of a local-scale UK climate model
Henry Addison, Elizabeth Kendon, Suman Ravuri +2
Climate change is causing the intensification of rainfall extremes. Precipitation projections with high spatial resolution are important for society to prepare for these changes, e…
Random initialisations performing above chance and how to find them
Frederik Benzing, Simon Schug, Robert Meier +5
Neural networks trained with stochastic gradient descent (SGD) starting from different random initialisations typically find functionally very similar solutions, raising the questi…
What deep reinforcement learning tells us about human motor learning and vice-versa
Michele Garibbo, Casimir Ludwig, Nathan Lepora +1
Machine learning and specifically reinforcement learning (RL) has been extremely successful in helping us to understand neural decision making processes. However, RL's role in unde…
Robustness to corruption in pre-trained Bayesian neural networks
Xi Wang, Laurence Aitchison
We develop ShiftMatch, a new training-data-dependent likelihood for robustness to corruption in Bayesian neural networks (BNNs). ShiftMatch is inspired by the training-data-depende…