Sentence examples for dynamics learning from inspiring English sources

This model makes response to the learner action by means of some equations relating to learning dynamics, learning energy, learning speed, learning force, and learning acceleration, which is analogous to the notion of Newtonian mechanics in some way; therefore, this model is named Learning Response Dynamics.

In the early stage of dynamics learning, humans tend to increase co-contraction and as learning progresses in consecutive reaching trials, a reduction in co-contraction along with a simultaneous reduction of the reaching errors made can be observed [4].

A limiting factor in OFC-LD is the dynamics learning using local methods, which on the one hand is an important precondition for the availability of heteroscedastic variances but on the other hand suffers from the curse of dimensionality, in that the learner has to produce a vast amount of training data to cover the whole state-action space.

Dynamics learning further provides us with means to model prediction uncertainty based on experienced stochastic movement data; we provide evidence that, in conjunction with an appropriate antagonistic arm and realistic motor variability model, impedance control emerges from a stochastic optimization process that minimizes these prediction uncertainties of the learned internal model.

We suggest that the concept of grasp-specific representations may provide a unifying framework for interpreting previous results related to dynamics learning.

The ultimate goal is to generate a comprehensive model incorporating mechanistic replication dynamics learned from virology with selection and complementation dynamics learned from population genetics.

In particular, Eq. 10 and Eq. 9 define a dynamical system with a mutual coupling of two distinct processes, in which neural dynamics and learning dynamics interact with distinct timescales.

As a growing body of work has continued to connect qualitative features of nonlinear dynamics and learning capacity [21 23], it is crucial to continue to further develop our understanding of how complex nonlinear dynamics emerges in structured heterogeneous networks.

The internal dynamics of learning contexts are then discussed initially in terms of principles, heuristics and scripts.

Aljadeff et al. [16] indeed find that the spectral radius is a good predictor of qualitative dynamics and learning capacity in networks with block-structured variances.

The idea is to give teachers access to important insight on stuff like class dynamics and learning trends, which they can then combine with assessment data, to improve their instruction or adapt to the way individual students learn.