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We introduce and analyze stochastic optimization methods where the input to each gradient update is perturbed by bounded noise.
An extension of representational machine learning methods where the algorithm uses multiple transformation layers between raw data and output rather than one layer.
We investigate SMC methods where the proposal distribution is computed by maximum likelihood or by a linearization approach.
Parallel two-step W-methods are linearly-implicit integration methods where the s stage values can be computed in parallel.
This is a very serious problem for data-based control design methods, where the plant is typically unknown.
By design, these techniques are intrinsically demand-controlled methods, where the amplitude of the stimulation signal is reduced when the desired desynchronized regime is reached.
Parameter estimation-based observers are a new kind of state reconstruction methods where the state observation task is translated into an on-line parameter estimation problem.
Indeed, the design of DoRiS was strongly based on formal methods, where the TLA+ language and its associated model-checker TLC were the supporting design tool.
As a result, the conservatism associated with the classical robust control methods where the controller is synthesized based on worst-case bounds is addressed.
Illustrative examples show the substantial improvements this method achieves over conventional methods where the tool path consists of linear or circular paths.
We discuss a general framework to describe generative dimensionality reduction methods, where the main focus lies on a regularized principal manifold learning variant.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com