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Found 7 papers, 2 papers with code
Minibatch training of neural network ensembles via trajectory sampling
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 decoupling the training time from the (often very large) size of the training datasets.
Combining Reinforcement Learning and Tensor Networks, with an Application to Dynamical Large Deviations
We present a framework to integrate tensor network (TN) methods with reinforcement learning (RL) for solving dynamical optimisation tasks.
Training neural network ensembles via trajectory sampling
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 than from a single larger model.
Reinforcement learning of rare diffusive dynamics
We present a method to probe rare molecular dynamics trajectories directly using reinforcement learning.
Symmetry-induced fluctuation relations in open quantum systems
We derive a general scheme to obtain quantum fluctuation relations for dynamical observables in open quantum systems.
Quantum Physics Statistical Mechanics
A reinforcement learning approach to rare trajectory sampling
By minimising the distance between a reweighted ensemble and that of a suitably parametrised controlled dynamics we arrive at a set of methods similar to those of RL to numerically approximate the optimal dynamics that realises the rare behaviour of interest.
A Tensor Network Approach to Finite Markov Decision Processes
Tensor network (TN) techniques - often used in the context of quantum many-body physics - have shown promise as a tool for tackling machine learning (ML) problems.
