49 citations · 64 across the 8 of their papers we have counts for
8 papers
Rare Event Analysis of Large Language Models
Jake McAllister Dorman, Edward Gillman, Dominic C. Rose +2
Being probabilistic models, during inference large language models (LLMs) display rare events: behaviour that is far from typical but highly significant. By definition all rare eve…
Spin models from nonlinear cellular automata
Konstantinos Sfairopoulos, Luke Causer, Jamie F. Mair +2
We study classical and quantum spin models derived from one-dimensional cellular automata (CA) with nonlinear update rules, focusing on rules 30, 54 and 201. We argue that the clas…
Cellular automata in dimensions and ground states of spin models in dimensions
Konstantinos Sfairopoulos, Luke Causer, Jamie F. Mair +1
We show how the trajectories of -dimensional cellular automata (CA) can be used to determine the ground states of -dimensional classical spin models, and we characterise…
Minibatch training of neural network ensembles via trajectory sampling
Jamie F. Mair, Luke Causer, Juan P. Garrahan
Most iterative neural network training methods use estimates of the loss function over small random subsets (or minibatches) of the data to update the parameters, which aid in deco…
Rejection-free quantum Monte Carlo in continuous time from transition path sampling
Luke Causer, Konstantinos Sfairopoulos, Jamie F. Mair +1
Continuous-time quantum Monte Carlo refers to a class of algorithms designed to sample the thermal distribution of a quantum Hamiltonian through exact expansions of the Boltzmann e…
Boundary conditions dependence of the phase transition in the quantum Newman-Moore model
Konstantinos Sfairopoulos, Luke Causer, Jamie F. Mair +1
We study the triangular plaquette model (TPM, also known as the Newman-Moore model) in the presence of a transverse magnetic field on a lattice with periodic boundaries in both spa…
Training neural network ensembles via trajectory sampling
Jamie F. Mair, Dominic C. Rose, Juan P. Garrahan
In machine learning, there is renewed interest in neural network ensembles (NNEs), whereby predictions are obtained as an aggregate from a diverse set of smaller models, rather tha…
A reinforcement learning approach to rare trajectory sampling
Dominic C. Rose, Jamie F. Mair, Juan P. Garrahan
Very often when studying non-equilibrium systems one is interested in analysing dynamical behaviour that occurs with very low probability, so called rare events. In practice, since…