decomposition
a9df2255ad642b923d95503b9a7958d8-Supplemental.pdf
All models discussed in the paper use ReLU non-linearity.
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Estimating Koopman operators with sketching to provably learn large scale dynamical systems
The theory of Koopman operators allows to deploy non-parametric machine learning algorithms to predict and analyze complex dynamical systems.
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Many-body Approximation for Non-negative Tensors
We present an alternative approach to decompose non-negative tensors, called many-body approximation .
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Sample Complexity of Goal-Conditioned Hierarchical Reinforcement Learning
HRL lead to improved sample complexity, and how much of an improvement can it provide? Theoretical work on sample-complexity bound in Machine Learning has been integral to the development of the field.
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