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16 papers · 1 filter
Deep Limits of Residual Neural Networks
Matthew Thorpe, Yves van Gennip
Neural networks have been very successful in many applications; we often, however, lack a theoretical understanding of what the neural networks are actually learning. This problem…
Boundary of the Range of a random walk and the Fölner property
George Deligiannidis, Sebastien Gouezel, Zemer Kosloff
The range process of a random walk is the collection of sites visited by the random walk up to time . In this work we deal with the question of whether the range process o…
Measures of path-based nonlinear expansion rates and Lagrangian uncertainty in stochastic flows
Michal Branicki, Kenneth Uda
We develop a probabilistic characterisation of trajectorial expansion rates in non-autonomous stochastic dynamical systems that can be defined over a finite time interval and used…
Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning
David Janz, Jiri Hron, Przemysław Mazur +3
Posterior sampling for reinforcement learning (PSRL) is an effective method for balancing exploration and exploitation in reinforcement learning. Randomised value functions (RVF) c…
The infinite Viterbi alignment and decay-convexity
Nick Whiteley, Matt W. Jones, Aleks P. F. Domanski
The infinite Viterbi alignment is the limiting maximum a-posteriori estimate of the unobserved path in a hidden Markov model as the length of the time horizon grows. For models on…
All-in-one: Multi-task Learning for Rumour Verification
Elena Kochkina, Maria Liakata, Arkaitz Zubiaga
Automatic resolution of rumours is a challenging task that can be broken down into smaller components that make up a pipeline, including rumour detection, rumour tracking and stanc…