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Found 10 papers, 0 papers with code
Recursive Filters as Linear Time-Invariant Systems
Recursive filters are treated as linear time-invariant (LTI) systems but they are not: uninitialised, they have an infinite number of outputs for any given input, while if initialised, they are not time-invariant.
On the tightness of information-theoretic bounds on generalization error of learning algorithms
However, such a learning rate is typically considered to be ``slow", compared to a ``fast rate" of $O(\lambda/n)$ in many learning scenarios.
An Information-Theoretic Analysis for Transfer Learning: Error Bounds and Applications
Specifically, we provide generalization error upper bounds for the empirical risk minimization (ERM) algorithm where data from both distributions are available in the training phase.
On Causality in Domain Adaptation and Semi-Supervised Learning: an Information-Theoretic Analysis
We show that in causal learning, the excess risk depends on the size of the source sample at a rate of O(1/m) only if the labelling distribution between the source and target domains remains unchanged.
Fast Rate Generalization Error Bounds: Variations on a Theme
However, such a learning rate is typically considered to be "slow", compared to a "fast rate" of O(1/n) in many learning scenarios.
A Bayesian Approach to (Online) Transfer Learning: Theory and Algorithms
Transfer learning is a machine learning paradigm where knowledge from one problem is utilized to solve a new but related problem.
Online Transfer Learning: Negative Transfer and Effect of Prior Knowledge
On the one hand, it is conceivable that knowledge from one task could be useful for solving a related problem.
Hidden Markov chains and fields with observations in Riemannian manifolds
Hidden Markov chain, or Markov field, models, with observations in a Euclidean space, play a major role across signal and image processing.
Statistics Theory Statistics Theory
New Insights on Learning Rules for Hopfield Networks: Memory and Objective Function Minimisation
Hopfield neural networks are a possible basis for modelling associative memory in living organisms.
Information-theoretic analysis for transfer learning
Specifically, we provide generalization error upper bounds for general transfer learning algorithms and extend the results to a specific empirical risk minimization (ERM) algorithm where data from both distributions are available in the training phase.
