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4 papers · 1 filter
Infinite Randomness Expansion and Amplification with a Constant Number of Devices
Matthew Coudron, Henry Yuen
We present a device-independent randomness expansion protocol, involving only a constant number of non-signaling quantum devices, that achieves \emph{infinite expansion}: starting…
Temporal Autoencoding Improves Generative Models of Time Series
Chris Häusler, Alex Susemihl, Martin P Nawrot +1
Restricted Boltzmann Machines (RBMs) are generative models which can learn useful representations from samples of a dataset in an unsupervised fashion. They have been widely employ…
A Greedy Approximation of Bayesian Reinforcement Learning with Probably Optimistic Transition Model
Kenji Kawaguchi, Mauricio Araya
Bayesian Reinforcement Learning (RL) is capable of not only incorporating domain knowledge, but also solving the exploration-exploitation dilemma in a natural way. As Bayesian RL i…
View-based propagation of decomposable constraints
Marco Correia, Pedro Barahona
Constraints that may be obtained by composition from simpler constraints are present, in some way or another, in almost every constraint program. The decomposition of such constrai…